1
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Yao HK, Mazza F, Prevot TD, Sibille E, Hay E. Spine loss in depression impairs dendritic signal integration in human cortical microcircuit models. iScience 2025; 28:112136. [PMID: 40292322 PMCID: PMC12032932 DOI: 10.1016/j.isci.2025.112136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Revised: 12/23/2024] [Accepted: 02/26/2025] [Indexed: 04/30/2025] Open
Abstract
Major depressive disorder (depression) is associated with altered dendritic structure and function of cortical pyramidal neurons, due to decreased inhibition from somatostatin (SST) interneurons and loss of spines and associated synapses, as indicated in postmortem human studies. Dendrites mediate signal processing through synaptic integration and nonlinear properties including backpropagating action potentials and dendritic Na+ spikes that enhance the neuron's computational power. However, it is currently unclear how depression-related dendritic changes impact signal integration. Here, we integrated human neuronal data of active dendritic properties and spine loss in depression into detailed computational models of human cortical microcircuits. We show that spine loss dampens signal response, worsening signal detection impairment than due to reduced SST interneuron inhibition alone. Furthermore, altered intrinsic properties due to spine loss abolished nonlinear dendritic signal integration and impaired recurrent microcircuit activity. Our study mechanistically links cellular changes in depression to impaired dendritic processing in human cortical microcircuits.
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Affiliation(s)
- Heng Kang Yao
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Thomas D. Prevot
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada
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2
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Wu S, Huang H, Wang S, Chen G, Zhou C, Yang D. Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics. SCIENCE ADVANCES 2025; 11:eadr3903. [PMID: 40173217 PMCID: PMC11963962 DOI: 10.1126/sciadv.adr3903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 02/26/2025] [Indexed: 04/04/2025]
Abstract
Cortical neuronal activity varies over time and across repeated trials, yet consistently represents stimulus features. The dynamical mechanism underlying this reliable representation and computation remains elusive. This study uncovers a mechanism for reliable neural information processing, leveraging a biologically plausible network model incorporating neural heterogeneity. First, we investigate neuronal timescale diversity, revealing that it disrupts intrinsic coherent spatiotemporal patterns, induces firing rate heterogeneity, enhances local responsive sensitivity, and aligns network activity closely with input. The system exhibits globally input-slaved transient dynamics, essential for reliable neural information processing. Other neural heterogeneities, such as nonuniform input connections, spike threshold heterogeneity, and network in-degree heterogeneity, play similar roles, highlighting the importance of neural heterogeneity in shaping consistent stimulus representation. This mechanism offers a potentially general framework for understanding neural heterogeneity in reliable computation and informs the design of reservoir computing models endowed with liquid wave reservoirs for neuromorphic computing.
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Affiliation(s)
- Shengdun Wu
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou 311100, China
| | - Haiping Huang
- PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Shengjun Wang
- Department of Physics, Shaanxi Normal University, Xi’an 710119, China
| | - Guozhang Chen
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China
| | - Changsong Zhou
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | - Dongping Yang
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou 311100, China
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3
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Poirier J, Beninger J, Naud R. Computational protocol for modeling and analyzing synaptic dynamics using SRPlasticity. STAR Protoc 2025; 6:103652. [PMID: 40029747 PMCID: PMC11915160 DOI: 10.1016/j.xpro.2025.103652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 12/09/2024] [Accepted: 02/05/2025] [Indexed: 03/21/2025] Open
Abstract
Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP. For complete details on the use and execution of this protocol, please refer to Rossbroich et al.1 and Beninger et al.2.
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Affiliation(s)
- Jade Poirier
- Center for Neural Dynamics and Artificial Intelligence, uOttawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - John Beninger
- Center for Neural Dynamics and Artificial Intelligence, uOttawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
| | - Richard Naud
- Center for Neural Dynamics and Artificial Intelligence, uOttawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Physics, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
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4
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Becker LA, Baccelli F, Taillefumier T. Subthreshold moment analysis of neuronal populations driven by synchronous synaptic inputs. ARXIV 2025:arXiv:2503.13702v1. [PMID: 40166746 PMCID: PMC11957229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Even when driven by the same stimulus, neuronal responses are well-known to exhibit a striking level of spiking variability. In-vivo electrophysiological recordings also reveal a surprisingly large degree of variability at the subthreshold level. In prior work, we considered biophysically relevant neuronal models to account for the observed magnitude of membrane voltage fluctuations. We found that accounting for these fluctuations requires weak but nonzero synchrony in the spiking activity, in amount that are consistent with experimentally measured spiking correlations. Here we investigate whether such synchrony can explain additional statistical features of the measured neural activity, including neuronal voltage covariability and voltage skewness. Addressing this question involves conducting a generalized moment analysis of conductance-based neurons in response to input drives modeled as correlated jump processes. Technically, we perform such an analysis using fixed-point techniques from queuing theory that are applicable in the stationary regime of activity. We found that weak but nonzero synchrony can consistently explain the experimentally reported voltage covariance and skewness. This confirms the role of synchrony as a primary driver of cortical variability and supports that physiological neural activity emerges as a population-level phenomenon, especially in the spontaneous regime.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Texas, USA
- Department of Neuroscience, The University of Texas at Austin, Texas, USA
| | - François Baccelli
- Department of Mathematics, The University of Texas at Austin, Texas, USA
- Departement d’informatique, Ecole Normale Supérieure, Paris, France
- Institut national de recherche en sciences et technologies du numérique, Paris, France
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Texas, USA
- Department of Neuroscience, The University of Texas at Austin, Texas, USA
- Department of Mathematics, The University of Texas at Austin, Texas, USA
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5
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Becker LA, Baccelli F, Taillefumier T. Subthreshold variability of neuronal populations driven by synchronous synaptic inputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.16.643547. [PMID: 40161748 PMCID: PMC11952518 DOI: 10.1101/2025.03.16.643547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Even when driven by the same stimulus, neuronal responses are well-known to exhibit a striking level of spiking variability. In-vivo electrophysiological recordings also reveal a surprisingly large degree of variability at the subthreshold level. In prior work, we considered biophysically relevant neuronal models to account for the observed magnitude of membrane voltage fluctuations. We found that accounting for these fluctuations requires weak but nonzero synchrony in the spiking activity, in amount that are consistent with experimentally measured spiking correlations. Here we investigate whether such synchrony can explain additional statistical features of the measured neural activity, including neuronal voltage covariability and voltage skewness. Addressing this question involves conducting a generalized moment analysis of conductance-based neurons in response to input drives modeled as correlated jump processes. Technically, we perform such an analysis using fixed-point techniques from queuing theory that are applicable in the stationary regime of activity. We found that weak but nonzero synchrony can consistently explain the experimentally reported voltage covariance and skewness. This confirms the role of synchrony as a primary driver of cortical variability and supports that physiological neural activity emerges as a population-level phenomenon, especially in the spontaneous regime.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Texas, USA
- Department of Neuroscience, The University of Texas at Austin, Texas, USA
| | - François Baccelli
- Department of Mathematics, The University of Texas at Austin, Texas, USA
- Departement d’informatique, Ecole Normale Supérieure, Paris, France
- Institut national de recherche en sciences et technologies du numérique, Paris, France
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Texas, USA
- Department of Neuroscience, The University of Texas at Austin, Texas, USA
- Department of Mathematics, The University of Texas at Austin, Texas, USA
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6
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Koren V, Blanco Malerba S, Schwalger T, Panzeri S. Efficient coding in biophysically realistic excitatory-inhibitory spiking networks. eLife 2025; 13:RP99545. [PMID: 40053385 PMCID: PMC11888603 DOI: 10.7554/elife.99545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2025] Open
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.
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Affiliation(s)
- Veronika Koren
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-EppendorfHamburgGermany
- Institute of Mathematics, Technische Universität BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
| | - Simone Blanco Malerba
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-EppendorfHamburgGermany
| | - Tilo Schwalger
- Institute of Mathematics, Technische Universität BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-EppendorfHamburgGermany
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7
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Liu B, White AJ, Lo CC. Augmenting flexibility: mutual inhibition between inhibitory neurons expands functional diversity. iScience 2025; 28:111718. [PMID: 39898045 PMCID: PMC11787539 DOI: 10.1016/j.isci.2024.111718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 11/06/2024] [Accepted: 12/27/2024] [Indexed: 02/04/2025] Open
Abstract
Recent advances in microcircuit analysis of nervous systems have revealed a plethora of mutual connections between inhibitory interneurons across many different species and brain regions. The abundance of these mutual connections has not been fully explained. Strikingly, we show that neural circuits with mutually inhibitory connections are able to rapidly and flexibly switch between distinct functions. That is, multiple functions coexist for a single set of synaptic weights. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a class of functional motifs we call coupled recurrent inhibitory and recurrent excitatory loops (CRIRELs). These CRIRELs have the advantage of being both multi-functional and controllable, performing a plethora of functions, including decisions, memory, toggle, and so forth. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.
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Affiliation(s)
- Belle Liu
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu City 30080, Taiwan
- Department of Physics, National Tsing Hua University, Hsinchu City 30080, Taiwan
| | - Alexander James White
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu City 30080, Taiwan
- International Intercollegiate Ph.D. Program, National Tsing Hua University, Hsinchu City 30080, Taiwan
| | - Chung-Chuan Lo
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu City 30080, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu City 30080, Taiwan
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8
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Saxena R, McNaughton BL. Bridging Neuroscience and AI: Environmental Enrichment as a model for forward knowledge transfer in continual learning. ARXIV 2025:arXiv:2405.07295v3. [PMID: 38947919 PMCID: PMC11213130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Continual learning (CL) refers to an agent's capability to learn from a continuous stream of data and transfer knowledge without forgetting old information. One crucial aspect of CL is forward transfer, i.e., improved and faster learning on a new task by leveraging information from prior knowledge. While this ability comes naturally to biological brains, it poses a significant challenge for artificial intelligence (AI). Here, we suggest that environmental enrichment (EE) can be used as a biological model for studying forward transfer, inspiring human-like AI development. EE refers to animal studies that enhance cognitive, social, motor, and sensory stimulation and is a model for what, in humans, is referred to as 'cognitive reserve'. Enriched animals show significant improvement in learning speed and performance on new tasks, typically exhibiting forward transfer. We explore anatomical, molecular, and neuronal changes post-EE and discuss how artificial neural networks (ANNs) can be used to predict neural computation changes after enriched experiences. Finally, we provide a synergistic way of combining neuroscience and AI research that paves the path toward developing AI capable of rapid and efficient new task learning.
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Affiliation(s)
- Rajat Saxena
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA
| | - Bruce L McNaughton
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA
- Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, T1K 3M4 Canada
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9
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Watson JF, Vargas-Barroso V, Morse-Mora RJ, Navas-Olive A, Tavakoli MR, Danzl JG, Tomschik M, Rössler K, Jonas P. Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory. Cell 2025; 188:501-514.e18. [PMID: 39667938 DOI: 10.1016/j.cell.2024.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 10/02/2024] [Accepted: 11/14/2024] [Indexed: 12/14/2024]
Abstract
Our brain has remarkable computational power, generating sophisticated behaviors, storing memories over an individual's lifetime, and producing higher cognitive functions. However, little of our neuroscience knowledge covers the human brain. Is this organ truly unique, or is it a scaled version of the extensively studied rodent brain? Combining multicellular patch-clamp recording with expansion-based superresolution microscopy and full-scale modeling, we determined the cellular and microcircuit properties of the human hippocampal CA3 region, a fundamental circuit for memory storage. In contrast to neocortical networks, human hippocampal CA3 displayed sparse connectivity, providing a circuit architecture that maximizes associational power. Human synapses showed unique reliability, high precision, and long integration times, exhibiting both species- and circuit-specific properties. Together with expanded neuronal numbers, these circuit characteristics greatly enhanced the memory storage capacity of CA3. Our results reveal distinct microcircuit properties of the human hippocampus and begin to unravel the inner workings of our most complex organ.
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Affiliation(s)
- Jake F Watson
- Institute of Science and Technology (ISTA), 3400 Klosterneuburg, Austria.
| | | | | | - Andrea Navas-Olive
- Institute of Science and Technology (ISTA), 3400 Klosterneuburg, Austria
| | - Mojtaba R Tavakoli
- Institute of Science and Technology (ISTA), 3400 Klosterneuburg, Austria
| | - Johann G Danzl
- Institute of Science and Technology (ISTA), 3400 Klosterneuburg, Austria
| | - Matthias Tomschik
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Karl Rössler
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Peter Jonas
- Institute of Science and Technology (ISTA), 3400 Klosterneuburg, Austria.
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10
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Koren V, Malerba SB, Schwalger T, Panzeri S. Efficient coding in biophysically realistic excitatory-inhibitory spiking networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.04.24.590955. [PMID: 38712237 PMCID: PMC11071478 DOI: 10.1101/2024.04.24.590955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.
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Affiliation(s)
- Veronika Koren
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
- Institute of Mathematics, Technische Universität Berlin, 10623 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Simone Blanco Malerba
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Tilo Schwalger
- Institute of Mathematics, Technische Universität Berlin, 10623 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
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11
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Yang Z, Teaney NA, Buttermore ED, Sahin M, Afshar-Saber W. Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders. Front Neurosci 2025; 18:1524577. [PMID: 39844857 PMCID: PMC11750789 DOI: 10.3389/fnins.2024.1524577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Accepted: 12/19/2024] [Indexed: 01/24/2025] Open
Abstract
Neurodevelopmental disorders (NDDs) affect 4.7% of the global population and are associated with delays in brain development and a spectrum of impairments that can lead to lifelong disability and even mortality. Identification of biomarkers for accurate diagnosis and medications for effective treatment are lacking, in part due to the historical use of preclinical model systems that do not translate well to the clinic for neurological disorders, such as rodents and heterologous cell lines. Human-induced pluripotent stem cells (hiPSCs) are a promising in vitro system for modeling NDDs, providing opportunities to understand mechanisms driving NDDs in human neurons. Functional assays, including patch clamping, multielectrode array, and imaging-based assays, are popular tools employed with hiPSC disease models for disease investigation. Recent progress in machine learning (ML) algorithms also presents unprecedented opportunities to advance the NDD research process. In this review, we compare two-dimensional and three-dimensional hiPSC formats for disease modeling, discuss the applications of functional assays, and offer insights on incorporating ML into hiPSC-based NDD research and drug screening.
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Affiliation(s)
- Ziqin Yang
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- FM Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Nicole A. Teaney
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- FM Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elizabeth D. Buttermore
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- FM Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Human Neuron Core, Boston Children’s Hospital, Boston, MA, United States
| | - Mustafa Sahin
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- FM Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Human Neuron Core, Boston Children’s Hospital, Boston, MA, United States
| | - Wardiya Afshar-Saber
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- FM Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
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12
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Chou CY, Wong HH, Guo C, Boukoulou KE, Huang C, Jannat J, Klimenko T, Li VY, Liang TA, Wu VC, Sjöström PJ. Principles of visual cortex excitatory microcircuit organization. Innovation (N Y) 2025; 6:100735. [PMID: 39872485 PMCID: PMC11763898 DOI: 10.1016/j.xinn.2024.100735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 11/13/2024] [Indexed: 01/30/2025] Open
Abstract
Synapse-specific connectivity and dynamics determine microcircuit function but are challenging to explore with classic paired recordings due to their low throughput. We therefore implemented optomapping, a ∼100-fold faster two-photon optogenetic method. In mouse primary visual cortex (V1), we optomapped 30,454 candidate inputs to reveal 1,790 excitatory inputs to pyramidal, basket, and Martinotti cells. Across these cell types, log-normal distribution of synaptic efficacies emerged as a principle. For pyramidal cells, optomapping reproduced the canonical circuit but unexpectedly uncovered that the excitation of basket cells concentrated to layer 5 and that of Martinotti cells dominated in layer 2/3. The excitation of basket cells was stronger and reached farther than the excitation of pyramidal cells, which may promote stability. Short-term plasticity surprisingly depended on cortical layer in addition to target cell. Finally, optomapping revealed an overrepresentation of shared inputs for interconnected layer-6 pyramidal cells. Thus, by resolving the throughput problem, optomapping uncovered hitherto unappreciated principles of V1 structure.
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Affiliation(s)
- Christina Y.C. Chou
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hovy H.W. Wong
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Connie Guo
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC H3A 2B4, Canada
| | - Kiminou E. Boukoulou
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Cleo Huang
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Javid Jannat
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Tal Klimenko
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Vivian Y. Li
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Tasha A. Liang
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - Vivian C. Wu
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
| | - P. Jesper Sjöström
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal, QC H3G 1A4, Canada
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13
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Guet-McCreight A, Mazza F, Prevot TD, Sibille E, Hay E. Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity. PLoS Comput Biol 2024; 20:e1012693. [PMID: 39729407 DOI: 10.1371/journal.pcbi.1012693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 12/03/2024] [Indexed: 12/29/2024] Open
Abstract
Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.
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Affiliation(s)
| | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Physiology, University of Toronto, Toronto, Canada
| | - Thomas D Prevot
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Physiology, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
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14
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Zarei Eskikand P, Soto-Breceda A, Cook MJ, Burkitt AN, Grayden DB. Neural dynamics and seizure correlations: Insights from neural mass models in a Tetanus Toxin rat model of epilepsy. Neural Netw 2024; 180:106746. [PMID: 39357176 DOI: 10.1016/j.neunet.2024.106746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/19/2024] [Accepted: 09/15/2024] [Indexed: 10/04/2024]
Abstract
This study focuses on the use of a neural mass model to investigate potential relationships between functional connectivity and seizure frequency in epilepsy. We fitted a three-layer neural mass model of a cortical column to intracranial EEG (iEEG) data from a Tetanus Toxin rat model of epilepsy, which also included responses to periodic electrical stimulation. Our results show that some of the connectivity weights between different neural populations correlate significantly with the number of seizures each day, offering valuable insights into the dynamics of neural circuits during epileptogenesis. We also simulated single-pulse electrical stimulation of the neuronal populations to observe their responses after the connectivity weights were optimized to fit background (non-seizure) EEG data. The recovery time, defined as the time from stimulation until the membrane potential returns to baseline, was measured as a representation of the critical slowing down phenomenon observed in nonlinear systems operating near a bifurcation boundary. The results revealed that recovery times in the responses of the computational model fitted to the EEG data were longer during 5 min periods preceding seizures compared to 1 hr before seizures in four out of six rats. Analysis of the iEEG recorded in response to electrical stimulation revealed results similar to the computational model in four out of six rats. This study supports the potential use of this computational model as a model-based biomarker for seizure prediction when direct electrical stimulation to the brain is not feasible.
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Affiliation(s)
- Parvin Zarei Eskikand
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Victoria, Australia.
| | - Artemio Soto-Breceda
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Mark J Cook
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital, Melbourne, Victoria, Australia
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15
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Dura-Bernal S, Herrera B, Lupascu C, Marsh BM, Gandolfi D, Marasco A, Neymotin S, Romani A, Solinas S, Bazhenov M, Hay E, Migliore M, Reinmann M, Arkhipov A. Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons. J Neurosci 2024; 44:e1236242024. [PMID: 39358017 PMCID: PMC11450527 DOI: 10.1523/jneurosci.1236-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/09/2024] [Accepted: 07/31/2024] [Indexed: 10/04/2024] Open
Abstract
Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.
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Affiliation(s)
- Salvador Dura-Bernal
- State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, New York 11203
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | | | - Carmen Lupascu
- Institute of Biophysics, National Research Council/Human Brain Project, Palermo 90146, Italy
| | - Brianna M Marsh
- University of California San Diego, La Jolla, California 92093
| | - Daniela Gandolfi
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | | | - Samuel Neymotin
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
- School of Medicine, New York University, New York 10012
| | - Armando Romani
- Swiss Federal Institute of Technology Lausanne (EPFL)/Blue Brain Project, Lausanne 1015, Switzerland
| | | | - Maxim Bazhenov
- University of California San Diego, La Jolla, California 92093
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
- University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Michele Migliore
- Institute of Biophysics, National Research Council/Human Brain Project, Palermo 90146, Italy
| | - Michael Reinmann
- Swiss Federal Institute of Technology Lausanne (EPFL)/Blue Brain Project, Lausanne 1015, Switzerland
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16
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Giannakakis E, Vinogradov O, Buendía V, Levina A. Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning. PLoS Comput Biol 2024; 20:e1012510. [PMID: 39480889 PMCID: PMC11556753 DOI: 10.1371/journal.pcbi.1012510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 11/12/2024] [Accepted: 09/25/2024] [Indexed: 11/02/2024] Open
Abstract
Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning and coding efficiency is hypothesized to be controlled by delicately balanced excitation and inhibition. These observations suggest a need for detailed co-tuning of excitatory and inhibitory populations. Theoretical studies have demonstrated that a combination of plasticity rules can lead to the emergence of excitation/inhibition (E/I) co-tuning in neurons driven by independent, low-noise signals. However, cortical signals are typically noisy and originate from highly recurrent networks, generating correlations in the inputs. This raises questions about the ability of plasticity mechanisms to self-organize co-tuned connectivity in neurons receiving noisy, correlated inputs. Here, we study the emergence of input selectivity and weight co-tuning in a neuron receiving input from a recurrent network via plastic feedforward connections. We demonstrate that while strong noise levels destroy the emergence of co-tuning in the readout neuron, introducing specific structures in the non-plastic pre-synaptic connectivity can re-establish it by generating a favourable correlation structure in the population activity. We further show that structured recurrent connectivity can impact the statistics in fully plastic recurrent networks, driving the formation of co-tuning in neurons that do not receive direct input from other areas. Our findings indicate that the network dynamics created by simple, biologically plausible structural connectivity patterns can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.
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Affiliation(s)
- Emmanouil Giannakakis
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Oleg Vinogradov
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Victor Buendía
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anna Levina
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
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17
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Ito S, Piet A, Bennett C, Durand S, Belski H, Garrett M, Olsen SR, Arkhipov A. Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics. Cell Rep 2024; 43:114763. [PMID: 39288028 PMCID: PMC11563561 DOI: 10.1016/j.celrep.2024.114763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 06/03/2024] [Accepted: 08/29/2024] [Indexed: 09/19/2024] Open
Abstract
Recent studies have found dramatic cell-type-specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at this granularity to understand brain function. Although initial work characterized activity by cell type, the alterations in cortical circuitry due to interacting novelty effects remain unclear. We investigated circuit mechanisms underlying the observed neural dynamics in response to novel stimuli using a large-scale public dataset of electrophysiological recordings in behaving mice and a population network model. The model was constrained by multi-patch synaptic physiology and electron microscopy data. We found generally weaker connections under novel stimuli, with shifts in the balance between somatostatin (SST) and vasoactive intestinal polypeptide (VIP) populations and increased excitatory influences on parvalbumin (PV) and SST populations. These findings systematically characterize how cortical circuits adapt to stimulus novelty.
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Affiliation(s)
| | - Alex Piet
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | | | | | - Hannah Belski
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | | | - Shawn R Olsen
- Allen Institute for Neural Dynamics, Seattle, WA, USA
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18
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Yang D, Qi G, Ort J, Witzig V, Bak A, Delev D, Koch H, Feldmeyer D. Modulation of large rhythmic depolarizations in human large basket cells by norepinephrine and acetylcholine. Commun Biol 2024; 7:885. [PMID: 39033173 PMCID: PMC11271271 DOI: 10.1038/s42003-024-06546-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 07/03/2024] [Indexed: 07/23/2024] Open
Abstract
Rhythmic brain activity is critical to many brain functions and is sensitive to neuromodulation, but so far very few studies have investigated this activity on the cellular level in vitro in human brain tissue samples. This study reveals and characterizes a novel rhythmic network activity in the human neocortex. Using intracellular patch-clamp recordings of human cortical neurons, we identify large rhythmic depolarizations (LRDs) driven by glutamate release but not by GABA. These LRDs are intricate events made up of multiple depolarizing phases, occurring at ~0.3 Hz, have large amplitudes and long decay times. Unlike human tissue, rat neocortex layers 2/3 exhibit no such activity under identical conditions. LRDs are mainly observed in a subset of L2/3 interneurons that receive substantial excitatory inputs and are likely large basket cells based on their morphology. LRDs are highly sensitive to norepinephrine (NE) and acetylcholine (ACh), two neuromodulators that affect network dynamics. NE increases LRD frequency through β-adrenergic receptor activity while ACh decreases it via M4 muscarinic receptor activation. Multi-electrode array recordings show that NE enhances and synchronizes oscillatory network activity, whereas ACh causes desynchronization. Thus, NE and ACh distinctly modulate LRDs, exerting specific control over human neocortical activity.
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Affiliation(s)
- Danqing Yang
- Research Center Juelich, Institute of Neuroscience and Medicine 10, Research Center Juelich, 52425, Juelich, Germany
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University Hospital, 52074, Aachen, Germany
| | - Guanxiao Qi
- Research Center Juelich, Institute of Neuroscience and Medicine 10, Research Center Juelich, 52425, Juelich, Germany
| | - Jonas Ort
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University Hospital, Aachen, Germany
- Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), RWTH Aachen University Hospital, 52074, Aachen, Germany
- Center for Integrated Oncology, Universities Aachen, Bonn, Cologne, Düsseldorf (CIO ABCD), Bonn, Germany
| | - Victoria Witzig
- Department of Neurology, RWTH Aachen University Hospital, 52074, Aachen, Germany
| | - Aniella Bak
- Department of Neurology, Section Epileptology, RWTH Aachen University Hospital, 52074, Aachen, Germany
| | - Daniel Delev
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University Hospital, Aachen, Germany
- Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), RWTH Aachen University Hospital, 52074, Aachen, Germany
- Center for Integrated Oncology, Universities Aachen, Bonn, Cologne, Düsseldorf (CIO ABCD), Bonn, Germany
| | - Henner Koch
- Department of Neurology, Section Epileptology, RWTH Aachen University Hospital, 52074, Aachen, Germany
| | - Dirk Feldmeyer
- Research Center Juelich, Institute of Neuroscience and Medicine 10, Research Center Juelich, 52425, Juelich, Germany.
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University Hospital, 52074, Aachen, Germany.
- Jülich-Aachen Research Alliance, Translational Brain Medicine (JARA Brain), Aachen, Germany.
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19
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Eskikand PZ, Cook MJ, Burkitt AN, Grayden DB. Reduced Synaptic Heterogeneity in a Tetanus Toxin Model of Epilepsy: Insights from Computational Modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2024; 2024:1-4. [PMID: 40040052 DOI: 10.1109/embc53108.2024.10782071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
A neural mass model was used to assess connectivity strength across diverse populations by fitting the model to background EEG data obtained from a Tetanus Toxin rat model of epilepsy. Our findings reveal a notable decline in the variability of estimated parameters when using EEG data recorded from rats in the Tetanus Toxin group compared with the control group. A detailed comparison of standard deviations in estimated parameters between day 1 and day 20 recordings, coinciding with a heightened number of seizures, underscores the impact of Tetanus Toxin on diminishing synaptic strength variability across recordings. This study supports electrophysiological studies suggesting that epileptogenesis induces a reduction in biophysical heterogeneity, potentially leading to an increase in network synchrony associated with epilepsy. Furthermore, our computational model establishes a foundation for future explorations of the implications of this diminished variability.
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20
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Mahon S. Variation and convergence in the morpho-functional properties of the mammalian neocortex. Front Syst Neurosci 2024; 18:1413780. [PMID: 38966330 PMCID: PMC11222651 DOI: 10.3389/fnsys.2024.1413780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 06/03/2024] [Indexed: 07/06/2024] Open
Abstract
Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.
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Affiliation(s)
- Séverine Mahon
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
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21
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Peng Y, Bjelde A, Aceituno PV, Mittermaier FX, Planert H, Grosser S, Onken J, Faust K, Kalbhenn T, Simon M, Radbruch H, Fidzinski P, Schmitz D, Alle H, Holtkamp M, Vida I, Grewe BF, Geiger JRP. Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit. Science 2024; 384:338-343. [PMID: 38635709 DOI: 10.1126/science.adg8828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
The computational capabilities of neuronal networks are fundamentally constrained by their specific connectivity. Previous studies of cortical connectivity have mostly been carried out in rodents; whether the principles established therein also apply to the evolutionarily expanded human cortex is unclear. We studied network properties within the human temporal cortex using samples obtained from brain surgery. We analyzed multineuron patch-clamp recordings in layer 2-3 pyramidal neurons and identified substantial differences compared with rodents. Reciprocity showed random distribution, synaptic strength was independent from connection probability, and connectivity of the supragranular temporal cortex followed a directed and mostly acyclic graph topology. Application of these principles in neuronal models increased dimensionality of network dynamics, suggesting a critical role for cortical computation.
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Affiliation(s)
- Yangfan Peng
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Antje Bjelde
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Pau Vilimelis Aceituno
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057 Zürich, Switzerland
| | - Franz X Mittermaier
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Henrike Planert
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Sabine Grosser
- Institute for Integrative Neuroanatomy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Julia Onken
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Katharina Faust
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Thilo Kalbhenn
- Department of Neurosurgery (Evangelisches Klinikum Bethel), Medical School, Bielefeld University, 33617 Bielefeld, Germany
| | - Matthias Simon
- Department of Neurosurgery (Evangelisches Klinikum Bethel), Medical School, Bielefeld University, 33617 Bielefeld, Germany
| | - Helena Radbruch
- Department of Neuropathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Pawel Fidzinski
- Clinical Study Center, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany
| | - Dietmar Schmitz
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Henrik Alle
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Martin Holtkamp
- Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Imre Vida
- Institute for Integrative Neuroanatomy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Benjamin F Grewe
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057 Zürich, Switzerland
| | - Jörg R P Geiger
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
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22
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Shen Y, Shao M, Hao ZZ, Huang M, Xu N, Liu S. Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity. Neurosci Bull 2024; 40:517-532. [PMID: 38194157 PMCID: PMC11003949 DOI: 10.1007/s12264-023-01160-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 09/23/2023] [Indexed: 01/10/2024] Open
Abstract
Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.
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Affiliation(s)
- Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mingting Shao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, 510080, China.
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23
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Lagzi F, Fairhall AL. Emergence of co-tuning in inhibitory neurons as a network phenomenon mediated by randomness, correlations, and homeostatic plasticity. SCIENCE ADVANCES 2024; 10:eadi4350. [PMID: 38507489 DOI: 10.1126/sciadv.adi4350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 02/15/2024] [Indexed: 03/22/2024]
Abstract
Cortical excitatory neurons show clear tuning to stimulus features, but the tuning properties of inhibitory interneurons are ambiguous. While inhibitory neurons have been considered to be largely untuned, some studies show that some parvalbumin-expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal neurons. In this study, we first use mean-field theory to demonstrate that a combination of homeostatic plasticity governing the synaptic dynamics of the connections from PV to excitatory neurons, heterogeneity in the excitatory postsynaptic potentials that impinge on PV neurons, and shared correlated input from layer 4 results in the functional and structural self-organization of PV subnetworks. Second, we show that structural and functional feature tuning of PV neurons emerges more clearly at the network level, i.e., that population-level measures identify functional and structural co-tuning of PV neurons that are not evident in pairwise individual-level measures. Finally, we show that such co-tuning can enhance network stability at the cost of reduced feature selectivity.
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Affiliation(s)
- Fereshteh Lagzi
- Department of Physiology and Biophysics, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195-7290, USA
- Computational Neuroscience Center, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195-7290, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195-7290, USA
- Computational Neuroscience Center, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195-7290, USA
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24
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Beninger J, Rossbroich J, Tóth K, Naud R. Functional subtypes of synaptic dynamics in mouse and human. Cell Rep 2024; 43:113785. [PMID: 38363673 DOI: 10.1016/j.celrep.2024.113785] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/08/2023] [Accepted: 01/27/2024] [Indexed: 02/18/2024] Open
Abstract
Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.
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Affiliation(s)
- John Beninger
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, ON K1H 8M5, Canada; uOttawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Julian Rossbroich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland; Faculty of Science, University of Basel, Basel, Switzerland
| | - Katalin Tóth
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, ON K1H 8M5, Canada; uOttawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Richard Naud
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, ON K1H 8M5, Canada; uOttawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Physics, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
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25
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Guet-McCreight A, Chameh HM, Mazza F, Prevot TD, Valiante TA, Sibille E, Hay E. In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression. Commun Biol 2024; 7:225. [PMID: 38396202 PMCID: PMC10891083 DOI: 10.1038/s42003-024-05907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.
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Affiliation(s)
- Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | | | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Thomas D Prevot
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, Toronto, ON, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, Toronto, ON, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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26
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs. PHYSICAL REVIEW. X 2024; 14:011021. [PMID: 38911939 PMCID: PMC11194039 DOI: 10.1103/physrevx.14.011021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Baowang Li
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Nicholas J. Priebe
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Eyal Seidemann
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, Texas 78712, USA
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27
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs. ARXIV 2023:arXiv:2304.09280v3. [PMID: 37131877 PMCID: PMC10153295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance ≃ 4 - 9 m V 2 ) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
| | - Baowang Li
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Center for Learning and Memory, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
| | - Nicholas J. Priebe
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Learning and Memory, The University of Texas at Austin
| | - Eyal Seidemann
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Department of Mathematics, The University of Texas at Austin
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28
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Suzuki M, Pennartz CMA, Aru J. How deep is the brain? The shallow brain hypothesis. Nat Rev Neurosci 2023; 24:778-791. [PMID: 37891398 DOI: 10.1038/s41583-023-00756-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.
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Affiliation(s)
- Mototaka Suzuki
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Cyriel M A Pennartz
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
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29
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Ito S, Piet A, Bennett C, Durand S, Belski H, Garrett M, Olsen SR, Arkhipov A. Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.21.563440. [PMID: 37961331 PMCID: PMC10634721 DOI: 10.1101/2023.10.21.563440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Recent studies have found dramatic cell-type specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at the cell-type specific level of granularity to understand brain function. Although initial work classified and characterized activity for each cell type, the specific alterations in cortical circuitry-particularly when multiple novelty effects interact-remain unclear. To address this gap, we employed a large-scale public dataset of electrophysiological recordings in the visual cortex of awake, behaving mice using Neuropixels probes and designed population network models to investigate the observed changes in neural dynamics in response to a combination of distinct forms of novelty. The model parameters were rigorously constrained by publicly available structural datasets, including multi-patch synaptic physiology and electron microscopy data. Our systematic optimization approach identified tens of thousands of model parameter sets that replicate the observed neural activity. Analysis of these solutions revealed generally weaker connections under novel stimuli, as well as a shift in the balance e between SST and VIP populations. Along with this, PV and SST populations experienced overall more excitatory influences compared to excitatory and VIP populations. Our results also highlight the role of VIP neurons in multiple aspects of visual stimulus processing and altering gain and saturation dynamics under novel conditions. In sum, our findings provide a systematic characterization of how the cortical circuit adapts to stimulus novelty by combining multiple rich public datasets.
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30
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Wilbers R, Galakhova AA, Driessens SL, Heistek TS, Metodieva VD, Hagemann J, Heyer DB, Mertens EJ, Deng S, Idema S, de Witt Hamer PC, Noske DP, van Schie P, Kommers I, Luan G, Li T, Shu Y, de Kock CP, Mansvelder HD, Goriounova NA. Structural and functional specializations of human fast-spiking neurons support fast cortical signaling. SCIENCE ADVANCES 2023; 9:eadf0708. [PMID: 37824618 PMCID: PMC10569701 DOI: 10.1126/sciadv.adf0708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/17/2023] [Indexed: 10/14/2023]
Abstract
Fast-spiking interneurons (FSINs) provide fast inhibition that synchronizes neuronal activity and is critical for cognitive function. Fast synchronization frequencies are evolutionary conserved in the expanded human neocortex despite larger neuron-to-neuron distances that challenge fast input-output transfer functions of FSINs. Here, we test in human neurons from neurosurgery tissue, which mechanistic specializations of human FSINs explain their fast-signaling properties in human cortex. With morphological reconstructions, multipatch recordings, and biophysical modeling, we find that despite threefold longer dendritic path, human FSINs maintain fast inhibition between connected pyramidal neurons through several mechanisms: stronger synapse strength of excitatory inputs, larger dendrite diameter with reduced complexity, faster AP initiation, and faster and larger inhibitory output, while Na+ current activation/inactivation properties are similar. These adaptations underlie short input-output delays in fast inhibition of human pyramidal neurons through FSINs, explaining how cortical synchronization frequencies are conserved despite expanded and sparse network topology of human cortex.
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Affiliation(s)
- René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Anna A. Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Stan L.W. Driessens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Tim S. Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Verjinia D. Metodieva
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Jim Hagemann
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Djai B. Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Eline J. Mertens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Suixin Deng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Wai Street, Beijing 100875, China
- Department of Neurosurgery, Jinshan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 201508, China
| | - Sander Idema
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center, Amsterdam Brain Tumor Center, de Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Philip C. de Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center, Amsterdam Brain Tumor Center, de Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - David P. Noske
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center, Amsterdam Brain Tumor Center, de Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Paul van Schie
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center, Amsterdam Brain Tumor Center, de Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Ivar Kommers
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center, Amsterdam Brain Tumor Center, de Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Guoming Luan
- Department of Neurosurgery, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Xiangshan Yikesong 50, Beijing 100093, China
| | - Tianfu Li
- Department of Neurosurgery, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Xiangshan Yikesong 50, Beijing 100093, China
| | - Yousheng Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Wai Street, Beijing 100875, China
- Department of Neurosurgery, Jinshan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 201508, China
| | - Christiaan P.J. de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Huibert D. Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
| | - Natalia A. Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, de Boelelaan 1085, 1081 HV Amsterdam, Netherlands
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31
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Navarro P, Oweiss K. Compressive sensing of functional connectivity maps from patterned optogenetic stimulation of neuronal ensembles. PATTERNS (NEW YORK, N.Y.) 2023; 4:100845. [PMID: 37876895 PMCID: PMC10591201 DOI: 10.1016/j.patter.2023.100845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 04/04/2023] [Accepted: 08/25/2023] [Indexed: 10/26/2023]
Abstract
Mapping functional connectivity between neurons is an essential step toward probing the neural computations mediating behavior. Accurately determining synaptic connectivity maps in populations of neurons is challenging in terms of yield, accuracy, and experimental time. Here, we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two-photon cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we characterized mapping recall and precision as a function of network observability, sparsity, number of neurons stimulated, off-target stimulation, synaptic reliability, propagation latency, and network topology. We found that mapping can be achieved with far fewer measurements than the standard pairwise sequential approach, with network sparsity and synaptic reliability serving as primary determinants of the performance. Our results suggest a rapid and efficient method to reconstruct functional connectivity of sparsely connected neuronal networks.
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Affiliation(s)
- Phillip Navarro
- Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32611, USA
| | - Karim Oweiss
- Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32611, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
- Department of Neurology, University of Florida, Gainesville, FL 32611, USA
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL 32611, USA
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32
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Williams N, Ojanperä A, Siebenhühner F, Toselli B, Palva S, Arnulfo G, Kaski S, Palva JM. The influence of inter-regional delays in generating large-scale brain networks of phase synchronization. Neuroimage 2023; 279:120318. [PMID: 37572765 DOI: 10.1016/j.neuroimage.2023.120318] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/14/2023] [Accepted: 08/10/2023] [Indexed: 08/14/2023] Open
Abstract
Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.
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Affiliation(s)
- N Williams
- Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland.
| | - A Ojanperä
- Department of Computer Science, Aalto University, Finland
| | - F Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; BioMag laboratory, HUS Medical Imaging Center, Helsinki, Finland
| | - B Toselli
- Department of Informatics, Bioengineering, Robotics & Systems Engineering, University of Genoa, Italy
| | - S Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, School of Neuroscience & Psychology, University of Glasgow, United Kingdom
| | - G Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Department of Informatics, Bioengineering, Robotics & Systems Engineering, University of Genoa, Italy
| | - S Kaski
- Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland; Department of Computer Science, Aalto University, Finland; Department of Computer Science, University of Manchester, United Kingdom
| | - J M Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, School of Neuroscience & Psychology, University of Glasgow, United Kingdom
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33
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Zdeblick DN, Shea-Brown ET, Witten DM, Buice MA. Modeling functional cell types in spike train data. PLoS Comput Biol 2023; 19:e1011509. [PMID: 37824442 PMCID: PMC10569560 DOI: 10.1371/journal.pcbi.1011509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023] Open
Abstract
A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by grouping them into functionally relevant classes. Formally, we define a hierarchical generative model for cell types, single-cell parameters, and neural responses, and then derive an expectation-maximization algorithm with variational inference that maximizes the likelihood of the neural recordings. We apply this "simultaneous" method to estimate cell types and fit single-cell models from simulated data, and find that it accurately recovers the ground truth parameters. We then apply our approach to in vitro neural recordings from neurons in mouse primary visual cortex, and find that it yields improved prediction of single-cell activity. We demonstrate that the discovered cell-type clusters are well separated and generalizable, and thus amenable to interpretation. We then compare discovered cluster memberships with locational, morphological, and transcriptomic data. Our findings reveal the potential to improve models of neural responses by explicitly allowing for shared functional properties across neurons.
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Affiliation(s)
- Daniel N. Zdeblick
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, United States of America
| | - Eric T. Shea-Brown
- Department of Applied Math, University of Washington, Seattle, Washington, United States of America
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
| | - Daniela M. Witten
- Department of Statistics and Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Michael A. Buice
- Department of Applied Math, University of Washington, Seattle, Washington, United States of America
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
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Zimin IA, Kazantsev VB, Stasenko SV. Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory. Biomimetics (Basel) 2023; 8:422. [PMID: 37754173 PMCID: PMC10526164 DOI: 10.3390/biomimetics8050422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/10/2023] [Accepted: 09/03/2023] [Indexed: 09/28/2023] Open
Abstract
In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model's performance was evaluated using simulated data from visual change detection experiments conducted on mice. Comparisons were made between the proposed model, a recurrent neural network simulating short-term memory based on sustained neural activity, and a feedforward neural network with short-term synaptic depression (STPNet) trained to achieve the same performance level as the mice. The results revealed that incorporating astrocytic modulation of synaptic transmission enhanced the model's performance.
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Affiliation(s)
- Ilya A. Zimin
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (I.A.Z.); (V.B.K.)
| | - Victor B. Kazantsev
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (I.A.Z.); (V.B.K.)
- Laboratory of Neurobiomorphic Technologies, Moscow Institute of Physics and Technology, 117303 Moscow, Russia
| | - Sergey V. Stasenko
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (I.A.Z.); (V.B.K.)
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35
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.536739. [PMID: 37131647 PMCID: PMC10153111 DOI: 10.1101/2023.04.17.536739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance ≅ 4-9mV 2 ) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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36
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de Kock CPJ, Feldmeyer D. Shared and divergent principles of synaptic transmission between cortical excitatory neurons in rodent and human brain. Front Synaptic Neurosci 2023; 15:1274383. [PMID: 37731775 PMCID: PMC10508294 DOI: 10.3389/fnsyn.2023.1274383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023] Open
Abstract
Information transfer between principal neurons in neocortex occurs through (glutamatergic) synaptic transmission. In this focussed review, we provide a detailed overview on the strength of synaptic neurotransmission between pairs of excitatory neurons in human and laboratory animals with a specific focus on data obtained using patch clamp electrophysiology. We reach two major conclusions: (1) the synaptic strength, measured as unitary excitatory postsynaptic potential (or uEPSP), is remarkably consistent across species, cortical regions, layers and/or cell-types (median 0.5 mV, interquartile range 0.4-1.0 mV) with most variability associated with the cell-type specific connection studied (min 0.1-max 1.4 mV), (2) synaptic function cannot be generalized across human and rodent, which we exemplify by discussing the differences in anatomical and functional properties of pyramidal-to-pyramidal connections within human and rodent cortical layers 2 and 3. With only a handful of studies available on synaptic transmission in human, it is obvious that much remains unknown to date. Uncovering the shared and divergent principles of synaptic transmission across species however, will almost certainly be a pivotal step toward understanding human cognitive ability and brain function in health and disease.
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Affiliation(s)
- Christiaan P. J. de Kock
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dirk Feldmeyer
- Research Center Juelich, Institute of Neuroscience and Medicine, Jülich, Germany
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University Hospital, Aachen, Germany
- Jülich-Aachen Research Alliance, Translational Brain Medicine (JARA Brain), Aachen, Germany
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37
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Zarei Eskikand P, Soto-Breceda A, Cook MJ, Burkitt AN, Grayden DB. Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column. Neural Netw 2023; 166:296-312. [PMID: 37541162 DOI: 10.1016/j.neunet.2023.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/16/2023] [Accepted: 07/12/2023] [Indexed: 08/06/2023]
Abstract
Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their "paradoxical response", where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation-inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation-inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points.
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Affiliation(s)
- Parvin Zarei Eskikand
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.
| | - Artemio Soto-Breceda
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital, Melbourne, Victoria, Australia
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38
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Bernáez Timón L, Ekelmans P, Kraynyukova N, Rose T, Busse L, Tchumatchenko T. How to incorporate biological insights into network models and why it matters. J Physiol 2023; 601:3037-3053. [PMID: 36069408 DOI: 10.1113/jp282755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/24/2022] [Indexed: 11/08/2022] Open
Abstract
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.
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Affiliation(s)
- Laura Bernáez Timón
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
| | - Pierre Ekelmans
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Nataliya Kraynyukova
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Tobias Rose
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Laura Busse
- Division of Neurobiology, Faculty of Biology, LMU Munich, Munich, Germany
- Bernstein Center for Computational Neuroscience, Munich, Germany
| | - Tatjana Tchumatchenko
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
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39
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Haufler D, Ito S, Koch C, Arkhipov A. Simulations of cortical networks using spatially extended conductance-based neuronal models. J Physiol 2023; 601:3123-3139. [PMID: 36567262 PMCID: PMC10290729 DOI: 10.1113/jp284030] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022] Open
Abstract
The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.
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Affiliation(s)
| | - Shinya Ito
- Mindscope Program, Allen Institute, Seattle, 98109
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40
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Baek S, Park Y, Paik SB. Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex. PLoS Comput Biol 2023; 19:e1011343. [PMID: 37540638 PMCID: PMC10403141 DOI: 10.1371/journal.pcbi.1011343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 07/10/2023] [Indexed: 08/06/2023] Open
Abstract
Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key components to organize a "small-world network" optimized for each size of the visual cortex, enabling the cost-efficient integration of visual information. Using computational simulations of a biologically inspired model neural network, we found that sparse LRCs added to networks, combined with dense local connections, compose a small-world network and significantly enhance image classification performance. We confirmed that the performance of the network appeared to be strongly correlated with the small-world coefficient of the model network under various conditions. Our theoretical model demonstrates that the amount of LRCs to build a small-world network depends on each size of cortex and that LRCs are beneficial only when the size of the network exceeds a certain threshold. Our model simulation of various sizes of cortices validates this prediction and provides an explanation of the species-specific existence of LRCs in animal data. Our results provide insight into a biological strategy of the brain to balance functional performance and resource cost.
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Affiliation(s)
- Seungdae Baek
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Youngjin Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Se-Bum Paik
- Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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41
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Kim MH, Radaelli C, Thomsen ER, Monet D, Chartrand T, Jorstad NL, Mahoney JT, Taormina MJ, Long B, Baker K, Bakken TE, Campagnola L, Casper T, Clark M, Dee N, D'Orazi F, Gamlin C, Kalmbach BE, Kebede S, Lee BR, Ng L, Trinh J, Cobbs C, Gwinn RP, Keene CD, Ko AL, Ojemann JG, Silbergeld DL, Sorensen SA, Berg J, Smith KA, Nicovich PR, Jarsky T, Zeng H, Ting JT, Levi BP, Lein E. Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex. eLife 2023; 12:e81863. [PMID: 37249212 PMCID: PMC10332811 DOI: 10.7554/elife.81863] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 05/29/2023] [Indexed: 05/31/2023] Open
Abstract
Rodent studies have demonstrated that synaptic dynamics from excitatory to inhibitory neuron types are often dependent on the target cell type. However, these target cell-specific properties have not been well investigated in human cortex, where there are major technical challenges in reliably obtaining healthy tissue, conducting multiple patch-clamp recordings on inhibitory cell types, and identifying those cell types. Here, we take advantage of newly developed methods for human neurosurgical tissue analysis with multiple patch-clamp recordings, post-hoc fluorescent in situ hybridization (FISH), machine learning-based cell type classification and prospective GABAergic AAV-based labeling to investigate synaptic properties between pyramidal neurons and PVALB- vs. SST-positive interneurons. We find that there are robust molecular differences in synapse-associated genes between these neuron types, and that individual presynaptic pyramidal neurons evoke postsynaptic responses with heterogeneous synaptic dynamics in different postsynaptic cell types. Using molecular identification with FISH and classifiers based on transcriptomically identified PVALB neurons analyzed by Patch-seq, we find that PVALB neurons typically show depressing synaptic characteristics, whereas other interneuron types including SST-positive neurons show facilitating characteristics. Together, these data support the existence of target cell-specific synaptic properties in human cortex that are similar to rodent, thereby indicating evolutionary conservation of local circuit connectivity motifs from excitatory to inhibitory neurons and their synaptic dynamics.
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Affiliation(s)
- Mean-Hwan Kim
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Deja Monet
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | | | - Brian Long
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | - Tamara Casper
- Allen Institute for Brain ScienceSeattleUnited States
| | - Michael Clark
- Allen Institute for Brain ScienceSeattleUnited States
| | - Nick Dee
- Allen Institute for Brain ScienceSeattleUnited States
| | | | - Clare Gamlin
- Allen Institute for Brain ScienceSeattleUnited States
| | - Brian E Kalmbach
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Physiology & Biophysics, School of Medicine, University of WashingtonSeattleUnited States
| | - Sara Kebede
- Allen Institute for Brain ScienceSeattleUnited States
| | - Brian R Lee
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lindsay Ng
- Allen Institute for Brain ScienceSeattleUnited States
| | - Jessica Trinh
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - C Dirk Keene
- Department of Laboratory Medicine & Pathology, School of Medicine, University of WashingtonSeattleUnited States
| | - Andrew L Ko
- Department of Neurological Surgery, School of Medicine, University of WashingtonSeattleUnited States
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, School of Medicine, University of WashingtonSeattleUnited States
| | - Daniel L Silbergeld
- Department of Neurological Surgery, School of Medicine, University of WashingtonSeattleUnited States
| | | | - Jim Berg
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Tim Jarsky
- Allen Institute for Brain ScienceSeattleUnited States
| | - Hongkui Zeng
- Allen Institute for Brain ScienceSeattleUnited States
| | - Jonathan T Ting
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Physiology & Biophysics, School of Medicine, University of WashingtonSeattleUnited States
| | - Boaz P Levi
- Allen Institute for Brain ScienceSeattleUnited States
| | - Ed Lein
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Laboratory Medicine & Pathology, School of Medicine, University of WashingtonSeattleUnited States
- Department of Neurological Surgery, School of Medicine, University of WashingtonSeattleUnited States
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42
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Ekelmans P, Kraynyukovas N, Tchumatchenko T. Targeting operational regimes of interest in recurrent neural networks. PLoS Comput Biol 2023; 19:e1011097. [PMID: 37186668 DOI: 10.1371/journal.pcbi.1011097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 05/25/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, for spiking networks, it is challenging to predict which connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We establish a mapping between the stabilized supralinear network (SSN) and spiking activity which allows us to pinpoint the location in parameter space where these activity regimes occur. Notably, we find that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we show that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.
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Affiliation(s)
- Pierre Ekelmans
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Nataliya Kraynyukovas
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Institute of Experimental Epileptology and Cognition Research, Life and Brain Center, Universitätsklinikum Bonn, Bonn, Germany
| | - Tatjana Tchumatchenko
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Institute of Experimental Epileptology and Cognition Research, Life and Brain Center, Universitätsklinikum Bonn, Bonn, Germany
- Institute of physiological chemistry, Medical center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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43
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Schneider A, Azabou M, McDougall-Vigier L, Parks DF, Ensley S, Bhaskaran-Nair K, Nowakowski T, Dyer EL, Hengen KB. Transcriptomic cell type structures in vivo neuronal activity across multiple timescales. Cell Rep 2023; 42:112318. [PMID: 36995938 PMCID: PMC10539488 DOI: 10.1016/j.celrep.2023.112318] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 02/04/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.
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Affiliation(s)
- Aidan Schneider
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Mehdi Azabou
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | - David F Parks
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Sahara Ensley
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Kiran Bhaskaran-Nair
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Tomasz Nowakowski
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Eva L Dyer
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Keith B Hengen
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA.
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44
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Buchholz MO, Gastone Guilabert A, Ehret B, Schuhknecht GFP. How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output. PLoS Comput Biol 2023; 19:e1011046. [PMID: 37068099 PMCID: PMC10153727 DOI: 10.1371/journal.pcbi.1011046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/02/2023] [Accepted: 03/24/2023] [Indexed: 04/18/2023] Open
Abstract
Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.
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Affiliation(s)
- Moritz O Buchholz
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
| | | | - Benjamin Ehret
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
| | - Gregor F P Schuhknecht
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
- Department of Molecular and Cellular Biology, Harvard University Cambridge, Massachusetts, United States of America
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45
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Urdaneta ME, Kunigk NG, Peñaloza-Aponte JD, Currlin S, Malone IG, Fried SI, Otto KJ. Layer-dependent stability of intracortical recordings and neuronal cell loss. Front Neurosci 2023; 17:1096097. [PMID: 37090803 PMCID: PMC10113640 DOI: 10.3389/fnins.2023.1096097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
Intracortical recordings can be used to voluntarily control external devices via brain-machine interfaces (BMI). Multiple factors, including the foreign body response (FBR), limit the stability of these neural signals over time. Current clinically approved devices consist of multi-electrode arrays with a single electrode site at the tip of each shank, confining the recording interface to a single layer of the cortex. Advancements in manufacturing technology have led to the development of high-density electrodes that can record from multiple layers. However, the long-term stability of neural recordings and the extent of neuronal cell loss around the electrode across different cortical depths have yet to be explored. To answer these questions, we recorded neural signals from rats chronically implanted with a silicon-substrate microelectrode array spanning the layers of the cortex. Our results show the long-term stability of intracortical recordings varies across cortical depth, with electrode sites around L4-L5 having the highest stability. Using machine learning guided segmentation, our novel histological technique, DeepHisto, revealed that the extent of neuronal cell loss varies across cortical layers, with L2/3 and L4 electrodes having the largest area of neuronal cell loss. These findings suggest that interfacing depth plays a major role in the FBR and long-term performance of intracortical neuroprostheses.
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Affiliation(s)
- Morgan E. Urdaneta
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Nicolas G. Kunigk
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Jesus D. Peñaloza-Aponte
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Seth Currlin
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Ian G. Malone
- Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Shelley I. Fried
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Boston Veterans Affairs Healthcare System, Boston, MA, United States
| | - Kevin J. Otto
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
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46
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Printz Y, Patil P, Mahn M, Benjamin A, Litvin A, Levy R, Bringmann M, Yizhar O. Determinants of functional synaptic connectivity among amygdala-projecting prefrontal cortical neurons in male mice. Nat Commun 2023; 14:1667. [PMID: 36966143 PMCID: PMC10039875 DOI: 10.1038/s41467-023-37318-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/13/2023] [Indexed: 03/27/2023] Open
Abstract
The medial prefrontal cortex (mPFC) mediates a variety of complex cognitive functions via its vast and diverse connections with cortical and subcortical structures. Understanding the patterns of synaptic connectivity that comprise the mPFC local network is crucial for deciphering how this circuit processes information and relays it to downstream structures. To elucidate the synaptic organization of the mPFC, we developed a high-throughput optogenetic method for mapping large-scale functional synaptic connectivity in acute brain slices. We show that in male mice, mPFC neurons that project to the basolateral amygdala (BLA) display unique spatial patterns of local-circuit synaptic connectivity, which distinguish them from the general mPFC cell population. When considering synaptic connections between pairs of mPFC neurons, the intrinsic properties of the postsynaptic cell and the anatomical positions of both cells jointly account for ~7.5% of the variation in the probability of connection. Moreover, anatomical distance and laminar position explain most of this fraction in variation. Our findings reveal the factors determining connectivity in the mPFC and delineate the architecture of synaptic connections in the BLA-projecting subnetwork.
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Affiliation(s)
- Yoav Printz
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Pritish Patil
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Mathias Mahn
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Asaf Benjamin
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Anna Litvin
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Rivka Levy
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Max Bringmann
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Ofer Yizhar
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel.
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47
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Hunt S, Leibner Y, Mertens EJ, Barros-Zulaica N, Kanari L, Heistek TS, Karnani MM, Aardse R, Wilbers R, Heyer DB, Goriounova NA, Verhoog MB, Testa-Silva G, Obermayer J, Versluis T, Benavides-Piccione R, de Witt-Hamer P, Idema S, Noske DP, Baayen JC, Lein ES, DeFelipe J, Markram H, Mansvelder HD, Schürmann F, Segev I, de Kock CPJ. Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex. Cereb Cortex 2023; 33:2857-2878. [PMID: 35802476 PMCID: PMC10016070 DOI: 10.1093/cercor/bhac246] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
Synaptic transmission constitutes the primary mode of communication between neurons. It is extensively studied in rodent but not human neocortex. We characterized synaptic transmission between pyramidal neurons in layers 2 and 3 using neurosurgically resected human middle temporal gyrus (MTG, Brodmann area 21), which is part of the distributed language circuitry. We find that local connectivity is comparable with mouse layer 2/3 connections in the anatomical homologue (temporal association area), but synaptic connections in human are 3-fold stronger and more reliable (0% vs 25% failure rates, respectively). We developed a theoretical approach to quantify properties of spinous synapses showing that synaptic conductance and voltage change in human dendritic spines are 3-4-folds larger compared with mouse, leading to significant NMDA receptor activation in human unitary connections. This model prediction was validated experimentally by showing that NMDA receptor activation increases the amplitude and prolongs decay of unitary excitatory postsynaptic potentials in human but not in mouse connections. Since NMDA-dependent recurrent excitation facilitates persistent activity (supporting working memory), our data uncovers cortical microcircuit properties in human that may contribute to language processing in MTG.
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Affiliation(s)
| | | | - Eline J Mertens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Natalí Barros-Zulaica
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Lida Kanari
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Mahesh M Karnani
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Romy Aardse
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | | | | | - Joshua Obermayer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Tamara Versluis
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Universidad Politécnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcón, Madrid 28223, Spain
| | - Philip de Witt-Hamer
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - Sander Idema
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - David P Noske
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - Johannes C Baayen
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Universidad Politécnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcón, Madrid 28223, Spain
| | - Henry Markram
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Felix Schürmann
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Idan Segev
- Department of Neurobiology and Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, 9190501 Jerusalem, Israel
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48
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Zdeblick DN, Shea-Brown ET, Witten DM, Buice MA. Modeling functional cell types in spike train data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530327. [PMID: 36909648 PMCID: PMC10002678 DOI: 10.1101/2023.02.28.530327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by grouping them into functionally relevant classes. Formally, we define a hierarchical generative model for cell types, single-cell parameters, and neural responses, and then derive an expectation-maximization algorithm with variational inference that maximizes the likelihood of the neural recordings. We apply this "simultaneous" method to estimate cell types and fit single-cell models from simulated data, and find that it accurately recovers the ground truth parameters. We then apply our approach to in vitro neural recordings from neurons in mouse primary visual cortex, and find that it yields improved prediction of single-cell activity. We demonstrate that the discovered cell-type clusters are well separated and generalizable, and thus amenable to interpretation. We then compare discovered cluster memberships with locational, morphological, and transcriptomic data. Our findings reveal the potential to improve models of neural responses by explicitly allowing for shared functional properties across neurons.
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Affiliation(s)
| | | | | | - Michael A. Buice
- Applied Math, University of Washington
- Allen Institute MindScope Program
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49
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Lin Y, Zhang XJ, Yang J, Li S, Li L, Lv X, Ma J, Shi SH. Developmental neuronal origin regulates neocortical map formation. Cell Rep 2023; 42:112170. [PMID: 36842085 DOI: 10.1016/j.celrep.2023.112170] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 12/14/2022] [Accepted: 02/10/2023] [Indexed: 02/27/2023] Open
Abstract
Sensory neurons in the neocortex exhibit distinct functional selectivity to constitute the neural map. While neocortical map of the visual cortex in higher mammals is clustered, it displays a striking "salt-and-pepper" pattern in rodents. However, little is known about the origin and basis of the interspersed neocortical map. Here we report that the intricate excitatory neuronal kinship-dependent synaptic connectivity influences precise functional map organization in the mouse primary visual cortex. While sister neurons originating from the same neurogenic radial glial progenitors (RGPs) preferentially develop synapses, cousin neurons derived from amplifying RGPs selectively antagonize horizontal synapse formation. Accordantly, cousin neurons in similar layers exhibit clear functional selectivity differences, contributing to a salt-and-pepper architecture. Removal of clustered protocadherins (cPCDHs), the largest subgroup of the diverse cadherin superfamily, eliminates functional selectivity differences between cousin neurons and alters neocortical map organization. These results suggest that developmental neuronal origin regulates neocortical map formation via cPCDHs.
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Affiliation(s)
- Yang Lin
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xin-Jun Zhang
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Jiajun Yang
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Shuo Li
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Laura Li
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaohui Lv
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jian Ma
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Song-Hai Shi
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Chinese Institute for Brain Research, Beijing, China.
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50
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Rollenhagen A, Anstötz M, Zimmermann K, Kasugai Y, Sätzler K, Molnar E, Ferraguti F, Lübke JHR. Layer-specific distribution and expression pattern of AMPA- and NMDA-type glutamate receptors in the barrel field of the adult rat somatosensory cortex: a quantitative electron microscopic analysis. Cereb Cortex 2023; 33:2342-2360. [PMID: 35732315 PMCID: PMC9977369 DOI: 10.1093/cercor/bhac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022] Open
Abstract
AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) and NMDA (N-methyl-d-aspartate) glutamate receptors are driving forces for synaptic transmission and plasticity at neocortical synapses. However, their distribution pattern in the adult rat neocortex is largely unknown and was quantified using freeze fracture replication combined with postimmunogold-labeling. Both receptors were co-localized at layer (L)4 and L5 postsynaptic densities (PSDs). At L4 dendritic shaft and spine PSDs, the number of gold grains detecting AMPA was similar, whereas at L5 shaft PSDs AMPA-receptors outnumbered those on spine PSDs. Their number was significantly higher at L5 vs. L4 PSDs. At L4 and L5 dendritic shaft PSDs, the number of gold grains detecting GluN1 was ~2-fold higher than at spine PSDs. The number of gold grains detecting the GluN1-subunit was higher for both shaft and spine PSDs in L5 vs. L4. Both receptors showed a large variability in L4 and L5. A high correlation between the number of gold grains and PSD size for both receptors and targets was observed. Both receptors were distributed over the entire PSD but showed a layer- and target-specific distribution pattern. The layer- and target-specific distribution of AMPA and GluN1 glutamate receptors partially contribute to the observed functional differences in synaptic transmission and plasticity in the neocortex.
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Affiliation(s)
- Astrid Rollenhagen
- Institute of Neuroscience and Medicine INM-10, Research Centre Jülich GmbH, Leo Brandt Str., Jülich 52425, Germany
| | - Max Anstötz
- Institute of Neuroscience and Medicine INM-10, Research Centre Jülich GmbH, Leo Brandt Str., Jülich 52425, Germany.,Institute of Anatomy II, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Universitätsstr. 1, Düsseldorf 40001, Germany
| | - Kerstin Zimmermann
- Institute of Neuroscience and Medicine INM-10, Research Centre Jülich GmbH, Leo Brandt Str., Jülich 52425, Germany
| | - Yu Kasugai
- Department of Pharmacology, Medical University of Innsbruck, Peter Mayr Strasse 1a, Innsbruck A-6020, Austria
| | - Kurt Sätzler
- School of Biomedical Sciences, University of Ulster, Cromore Rd., Londonderry BT52 1SA, United Kingdom
| | - Elek Molnar
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, University Walk, Bristol BS8 1TD, United Kingdom
| | - Francesco Ferraguti
- Department of Pharmacology, Medical University of Innsbruck, Peter Mayr Strasse 1a, Innsbruck A-6020, Austria
| | - Joachim H R Lübke
- Institute of Neuroscience and Medicine INM-10, Research Centre Jülich GmbH, Leo Brandt Str., Jülich 52425, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH/Medical University Aachen, Pauwelstr. 30, Aachen 52074, Germany.,JARA Translational Medicine Jülich/Aachen, Germany
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