1
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Xiao S, Yadav S, Jayant K. Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging. Cell Rep 2024; 43:114413. [PMID: 38943640 DOI: 10.1016/j.celrep.2024.114413] [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: 09/29/2022] [Revised: 05/06/2024] [Accepted: 06/12/2024] [Indexed: 07/01/2024] Open
Abstract
Basal dendrites of layer 5 cortical pyramidal neurons exhibit Na+ and N-methyl-D-aspartate receptor (NMDAR) regenerative spikes and are uniquely poised to influence somatic output. Nevertheless, due to technical limitations, how multibranch basal dendritic integration shapes and enables multiplexed barcoding of synaptic streams remains poorly mapped. Here, we combine 3D two-photon holographic transmitter uncaging, whole-cell dynamic clamp, and biophysical modeling to reveal how synchronously activated synapses (distributed and clustered) across multiple basal dendritic branches are multiplexed under quiescent and in vivo-like conditions. While dendritic regenerative Na+ spikes promote millisecond somatic spike precision, distributed synaptic inputs and NMDAR spikes regulate gain. These concomitantly occurring dendritic nonlinearities enable multiplexed information transfer amid an ongoing noisy background, including under back-propagating voltage resets, by barcoding the axo-somatic spike structure. Our results unveil a multibranch dendritic integration framework in which dendritic nonlinearities are critical for multiplexing different spatial-temporal synaptic input patterns, enabling optimal feature binding.
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Affiliation(s)
- Shulan Xiao
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Saumitra Yadav
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Krishna Jayant
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA.
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2
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Jauch J, Becker M, Tetzlaff C, Fauth MJ. Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites. PLoS Comput Biol 2024; 20:e1012218. [PMID: 38917228 PMCID: PMC11230591 DOI: 10.1371/journal.pcbi.1012218] [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: 09/22/2023] [Revised: 07/08/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.
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Affiliation(s)
- Jannik Jauch
- Third Institute for Physics, Georg-August-University, Göttingen, Germany
| | - Moritz Becker
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | - Christian Tetzlaff
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | - Michael Jan Fauth
- Third Institute for Physics, Georg-August-University, Göttingen, Germany
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3
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Ecker A, Egas Santander D, Bolaños-Puchet S, Isbister JB, Reimann MW. Cortical cell assemblies and their underlying connectivity: An in silico study. PLoS Comput Biol 2024; 20:e1011891. [PMID: 38466752 PMCID: PMC10927091 DOI: 10.1371/journal.pcbi.1011891] [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: 11/09/2023] [Accepted: 02/05/2024] [Indexed: 03/13/2024] Open
Abstract
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.
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Affiliation(s)
- András Ecker
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Daniela Egas Santander
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Sirio Bolaños-Puchet
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James B. Isbister
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Michael W. Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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4
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Cornean J, Molina-Obando S, Gür B, Bast A, Ramos-Traslosheros G, Chojetzki J, Lörsch L, Ioannidou M, Taneja R, Schnaitmann C, Silies M. Heterogeneity of synaptic connectivity in the fly visual system. Nat Commun 2024; 15:1570. [PMID: 38383614 PMCID: PMC10882054 DOI: 10.1038/s41467-024-45971-z] [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: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Visual systems are homogeneous structures, where repeating columnar units retinotopically cover the visual field. Each of these columns contain many of the same neuron types that are distinguished by anatomic, genetic and - generally - by functional properties. However, there are exceptions to this rule. In the 800 columns of the Drosophila eye, there is an anatomically and genetically identifiable cell type with variable functional properties, Tm9. Since anatomical connectivity shapes functional neuronal properties, we identified the presynaptic inputs of several hundred Tm9s across both optic lobes using the full adult female fly brain (FAFB) electron microscopic dataset and FlyWire connectome. Our work shows that Tm9 has three major and many sparsely distributed inputs. This differs from the presynaptic connectivity of other Tm neurons, which have only one major, and more stereotypic inputs than Tm9. Genetic synapse labeling showed that the heterogeneous wiring exists across individuals. Together, our data argue that the visual system uses heterogeneous, distributed circuit properties to achieve robust visual processing.
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Affiliation(s)
- Jacqueline Cornean
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Sebastian Molina-Obando
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Annika Bast
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonas Chojetzki
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Lena Lörsch
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Maria Ioannidou
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Rachita Taneja
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Christopher Schnaitmann
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany.
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5
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Petousakis KE, Park J, Papoutsi A, Smirnakis S, Poirazi P. Modeling apical and basal tree contribution to orientation selectivity in a mouse primary visual cortex layer 2/3 pyramidal cell. eLife 2023; 12:e91627. [PMID: 38054403 PMCID: PMC10754496 DOI: 10.7554/elife.91627] [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: 08/08/2023] [Accepted: 11/12/2023] [Indexed: 12/07/2023] Open
Abstract
Pyramidal neurons, a mainstay of cortical regions, receive a plethora of inputs from various areas onto their morphologically distinct apical and basal trees. Both trees differentially contribute to the somatic response, defining distinct anatomical and possibly functional sub-units. To elucidate the contribution of each tree to the encoding of visual stimuli at the somatic level, we modeled the response pattern of a mouse L2/3 V1 pyramidal neuron to orientation tuned synaptic input. Towards this goal, we used a morphologically detailed computational model of a single cell that replicates electrophysiological and two-photon imaging data. Our simulations predict a synergistic effect of apical and basal trees on somatic action potential generation: basal tree activity, in the form of either depolarization or dendritic spiking, is necessary for producing somatic activity, despite the fact that most somatic spikes are heavily driven by apical dendritic spikes. This model provides evidence for synergistic computations taking place in the basal and apical trees of the L2/3 V1 neuron along with mechanistic explanations for tree-specific contributions and emphasizes the potential role of predictive and attentional feedback input in these cells.
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Affiliation(s)
| | - Jiyoung Park
- Department of Neurology, Brigham and Women’s Hospital and Jamaica Plain Veterans Administration Hospital, Harvard Medical SchoolBostonUnited States
| | | | - Stelios Smirnakis
- Department of Neurology, Brigham and Women’s Hospital and Jamaica Plain Veterans Administration Hospital, Harvard Medical SchoolBostonUnited States
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6
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Roland PE. How far neuroscience is from understanding brains. Front Syst Neurosci 2023; 17:1147896. [PMID: 37867627 PMCID: PMC10585277 DOI: 10.3389/fnsys.2023.1147896] [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: 01/19/2023] [Accepted: 07/31/2023] [Indexed: 10/24/2023] Open
Abstract
The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary.
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Affiliation(s)
- Per E. Roland
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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7
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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: 5] [Impact Index Per Article: 5.0] [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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8
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Gallinaro JV, Scholl B, Clopath C. Synaptic weights that correlate with presynaptic selectivity increase decoding performance. PLoS Comput Biol 2023; 19:e1011362. [PMID: 37549193 PMCID: PMC10434873 DOI: 10.1371/journal.pcbi.1011362] [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: 11/28/2022] [Revised: 08/17/2023] [Accepted: 07/16/2023] [Indexed: 08/09/2023] Open
Abstract
The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.
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Affiliation(s)
- Júlia V. Gallinaro
- Bioengineering Department, Imperial College London, London, United Kingdom
| | - Benjamin Scholl
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania, United States of America
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, United Kingdom
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9
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Petousakis KE, Apostolopoulou AA, Poirazi P. The impact of Hodgkin-Huxley models on dendritic research. J Physiol 2023; 601:3091-3102. [PMID: 36218068 PMCID: PMC10600871 DOI: 10.1113/jp282756] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/16/2022] [Indexed: 11/08/2022] Open
Abstract
For the past seven decades, the Hodgkin-Huxley (HH) formalism has been an invaluable tool in the arsenal of neuroscientists, allowing for robust and reproducible modelling of ionic conductances and the electrophysiological phenomena they underlie. Despite its apparent age, its role as a cornerstone of computational neuroscience has not waned. The discovery of dendritic regenerative events mediated by ionic and synaptic conductances has solidified the importance of HH-based models further, yielding new predictions concerning dendritic integration, synaptic plasticity and neuronal computation. These predictions are often validated through in vivo and in vitro experiments, advancing our understanding of the neuron as a biological system and emphasizing the importance of HH-based detailed computational models as an instrument of dendritic research. In this article, we discuss recent studies in which the HH formalism is used to shed new light on dendritic function and its role in neuronal phenomena.
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Affiliation(s)
- Konstantinos-Evangelos Petousakis
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
- Department of Biology, University of Crete, Heraklion, Crete, Greece
| | - Anthi A Apostolopoulou
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
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10
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Mc Hugh J, Makarchuk S, Mozheiko D, Fernandez-Villegas A, Kaminski Schierle GS, Kaminski CF, Keyser UF, Holcman D, Rouach N. Diversity of dynamic voltage patterns in neuronal dendrites revealed by nanopipette electrophysiology. NANOSCALE 2023. [PMID: 37455621 PMCID: PMC10373629 DOI: 10.1039/d2nr03475a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Dendrites and dendritic spines are the essential cellular compartments in neuronal communication, conveying information through transient voltage signals. Our understanding of these compartmentalized voltage dynamics in fine, distal neuronal dendrites remains poor due to the difficulties inherent to accessing and stably recording from such small, nanoscale cellular compartments for a sustained time. To overcome these challenges, we use nanopipettes that permit long and stable recordings directly from fine neuronal dendrites. We reveal a diversity of voltage dynamics present locally in dendrites, such as spontaneous voltage transients, bursting events and oscillating periods of silence and firing activity, all of which we characterized using segmentation analysis. Remarkably, we find that neuronal dendrites can display spontaneous hyperpolarisation events, and sustain transient hyperpolarised states. The voltage patterns were activity-dependent, with a stronger dependency on synaptic activity than on action potentials. Long-time recordings of fine dendritic protrusions show complex voltage dynamics that may represent a previously unexplored contribution to dendritic computations.
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Affiliation(s)
- Jeffrey Mc Hugh
- Centre for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Université PSL, Labex Memolife, Paris, France.
- Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
| | - Stanislaw Makarchuk
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Daria Mozheiko
- Centre for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Université PSL, Labex Memolife, Paris, France.
- Doctoral School No 158, Sorbonne Université, Paris, France
| | - Ana Fernandez-Villegas
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Gabriele S Kaminski Schierle
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Clemens F Kaminski
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Ulrich F Keyser
- Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
| | - David Holcman
- Group Data Modelling, Computational Biology and Predictive Medicine, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS, INSERM, Université PSL, Labex Memolife, Paris, France
- Churchill College, University of Cambridge, Cambridge CB3 0DS, UK
| | - Nathalie Rouach
- Centre for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Université PSL, Labex Memolife, Paris, France.
- Churchill College, University of Cambridge, Cambridge CB3 0DS, UK
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11
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Aggarwal A, Liu R, Chen Y, Ralowicz AJ, Bergerson SJ, Tomaska F, Mohar B, Hanson TL, Hasseman JP, Reep D, Tsegaye G, Yao P, Ji X, Kloos M, Walpita D, Patel R, Mohr MA, Tillberg PW, Looger LL, Marvin JS, Hoppa MB, Konnerth A, Kleinfeld D, Schreiter ER, Podgorski K. Glutamate indicators with improved activation kinetics and localization for imaging synaptic transmission. Nat Methods 2023; 20:925-934. [PMID: 37142767 PMCID: PMC10250197 DOI: 10.1038/s41592-023-01863-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 03/21/2023] [Indexed: 05/06/2023]
Abstract
The fluorescent glutamate indicator iGluSnFR enables imaging of neurotransmission with genetic and molecular specificity. However, existing iGluSnFR variants exhibit low in vivo signal-to-noise ratios, saturating activation kinetics and exclusion from postsynaptic densities. Using a multiassay screen in bacteria, soluble protein and cultured neurons, we generated variants with improved signal-to-noise ratios and kinetics. We developed surface display constructs that improve iGluSnFR's nanoscopic localization to postsynapses. The resulting indicator iGluSnFR3 exhibits rapid nonsaturating activation kinetics and reports synaptic glutamate release with decreased saturation and increased specificity versus extrasynaptic signals in cultured neurons. Simultaneous imaging and electrophysiology at individual boutons in mouse visual cortex showed that iGluSnFR3 transients report single action potentials with high specificity. In vibrissal sensory cortex layer 4, we used iGluSnFR3 to characterize distinct patterns of touch-evoked feedforward input from thalamocortical boutons and both feedforward and recurrent input onto L4 cortical neuron dendritic spines.
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Affiliation(s)
- Abhi Aggarwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Rui Liu
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | - Yang Chen
- Institute of Neuroscience and Cluster for Systems Neurology (SyNergy), Technical University of Munich (TUM), Munich, Germany
| | - Amelia J Ralowicz
- Department of Biological Sciences, Dartmouth College, Hanover, NH, USA
| | | | - Filip Tomaska
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Boaz Mohar
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Timothy L Hanson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jeremy P Hasseman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Daniel Reep
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Getahun Tsegaye
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Pantong Yao
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Xiang Ji
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | - Marinus Kloos
- Institute of Neuroscience and Cluster for Systems Neurology (SyNergy), Technical University of Munich (TUM), Munich, Germany
| | - Deepika Walpita
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ronak Patel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Manuel A Mohr
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH) Zurich, Basel, Switzerland
| | - Paul W Tillberg
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Loren L Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Howard Hughes Medical Institute, Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Jonathan S Marvin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michael B Hoppa
- Department of Biological Sciences, Dartmouth College, Hanover, NH, USA
| | - Arthur Konnerth
- Institute of Neuroscience and Cluster for Systems Neurology (SyNergy), Technical University of Munich (TUM), Munich, Germany
| | - David Kleinfeld
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
- Section of Neurobiology, University of California, San Diego, La Jolla, CA, USA
| | - Eric R Schreiter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Kaspar Podgorski
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Allen Institute for Neural Dynamics, Seattle, WA, USA.
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12
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Wong-Campos JD, Park P, Davis H, Qi Y, Tian H, Itkis DG, Kim D, Grimm JB, Plutkis SE, Lavis L, Cohen AE. Voltage dynamics of dendritic integration and back-propagation in vivo. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.25.542363. [PMID: 37292691 PMCID: PMC10245993 DOI: 10.1101/2023.05.25.542363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Neurons integrate synaptic inputs within their dendrites and produce spiking outputs, which then propagate down the axon and back into the dendrites where they contribute to plasticity. Mapping the voltage dynamics in dendritic arbors of live animals is crucial for understanding neuronal computation and plasticity rules. Here we combine patterned channelrhodopsin activation with dual-plane structured illumination voltage imaging, for simultaneous perturbation and monitoring of dendritic and somatic voltage in Layer 2/3 pyramidal neurons in anesthetized and awake mice. We examined the integration of synaptic inputs and compared the dynamics of optogenetically evoked, spontaneous, and sensory-evoked back-propagating action potentials (bAPs). Our measurements revealed a broadly shared membrane voltage throughout the dendritic arbor, and few signatures of electrical compartmentalization among synaptic inputs. However, we observed spike rate acceleration-dependent propagation of bAPs into distal dendrites. We propose that this dendritic filtering of bAPs may play a critical role in activity-dependent plasticity.
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Affiliation(s)
- J David Wong-Campos
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Pojeong Park
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Hunter Davis
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Yitong Qi
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - He Tian
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Daniel G Itkis
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Doyeon Kim
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Jonathan B Grimm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sarah E Plutkis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Luke Lavis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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13
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Kim YJ, Ujfalussy BB, Lengyel M. Parallel functional architectures within a single dendritic tree. Cell Rep 2023; 42:112386. [PMID: 37060564 PMCID: PMC7614531 DOI: 10.1016/j.celrep.2023.112386] [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: 02/24/2022] [Revised: 10/31/2022] [Accepted: 03/28/2023] [Indexed: 04/16/2023] Open
Abstract
The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, such that each synaptic input makes a single contribution to the neuronal response. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel. We used statistically principled methods to fit flexible, yet interpretable, models of the transformation of input spikes into the somatic "output" voltage and to automatically select among alternative functional architectures. With dendritic Na+ channels blocked, responses are accurately captured by a single static and global nonlinearity. In contrast, dendritic Na+-dependent integration requires a functional architecture with multiple dynamic nonlinearities and clustered connectivity. These two architectures incorporate distinct morphological and biophysical properties of the neuron and its synaptic organization.
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Affiliation(s)
- Young Joon Kim
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Harvard Medical School, Boston, MA, USA.
| | - Balázs B Ujfalussy
- Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
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14
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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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15
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Gooch HM, Bluett T, Perumal MB, Vo HD, Fletcher LN, Papacostas J, Jeffree RL, Wood M, Colditz MJ, McMillen J, Tsahtsarlis T, Amato D, Campbell R, Gillinder L, Williams SR. High-fidelity dendritic sodium spike generation in human layer 2/3 neocortical pyramidal neurons. Cell Rep 2022; 41:111500. [DOI: 10.1016/j.celrep.2022.111500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/22/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022] Open
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16
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Yates JL, Scholl B. Unraveling Functional Diversity of Cortical Synaptic Architecture Through the Lens of Population Coding. Front Synaptic Neurosci 2022; 14:888214. [PMID: 35957943 PMCID: PMC9360921 DOI: 10.3389/fnsyn.2022.888214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/21/2022] [Indexed: 11/15/2022] Open
Abstract
The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. One possibility is that this diversity reflects the propagation of information from one neural population to another. To test this possibility, we bridge population coding theory with measurements of synaptic inputs recorded in vivo with two-photon calcium imaging. We construct a probabilistic decoder to estimate the stimulus orientation from the responses of a realistic, hypothetical input population of neurons to compare with synaptic inputs onto individual neurons of ferret primary visual cortex (V1) recorded with two-photon calcium imaging in vivo. We find that optimal decoding requires diverse input weights and provides a straightforward mapping from the decoder weights to excitatory synapses. Analytically derived weights for biologically realistic input populations closely matched the functional heterogeneity of dendritic spines imaged in vivo with two-photon calcium imaging. Our results indicate that synaptic diversity is a necessary component of information transmission and reframes studies of connectivity through the lens of probabilistic population codes. These results suggest that the mapping from synaptic inputs to somatic selectivity may not be directly interpretable without considering input covariance and highlights the importance of population codes in pursuit of the cortical connectome.
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Affiliation(s)
- Jacob L. Yates
- Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, Berkeley, CA, United States
| | - Benjamin Scholl
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Benjamin Scholl
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17
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Jedlicka P, Bird AD, Cuntz H. Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons. Open Biol 2022; 12:220073. [PMID: 35857898 PMCID: PMC9277232 DOI: 10.1098/rsob.220073] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.
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Affiliation(s)
- Peter Jedlicka
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany,Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, Frankfurt/Main, Germany,Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Alexander D. Bird
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany,Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - Hermann Cuntz
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
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18
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Application of the mirror technique for block-face scanning electron microscopy. Brain Struct Funct 2022; 227:1933-1947. [PMID: 35643821 PMCID: PMC9232443 DOI: 10.1007/s00429-022-02506-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/04/2022] [Indexed: 11/29/2022]
Abstract
The mirror technique adapted for electron microscopy allows correlating neuronal structures across the cutting plane of adjoining light microscopic sections which, however, have a limited thickness, typically less than 100 µm (Talapka et al. in Front Neuroanat, 2021, 10.3389/fnana.2021.652422). Here, we extend the mirror technique for tissue blocks in the millimeter range and demonstrate compatibility with serial block-face electron microscopy (SBEM). An essential step of the methodological improvement regards the recognition that unbound resin must be removed from the tissue surface to gain visibility of surface structures. To this, the tissue block was placed on absorbent paper during the curing process. In this way, neuronal cell bodies could be unequivocally identified using epi-illumination and confocal microscopy. Thus, the layout of cell bodies which were cut by the sectioning plane can be correlated with the layout of their complementary part in the adjoining section processed for immunohistochemistry. The modified mirror technique obviates the spatial limit in investigating synaptology of neurochemically identified structures such as neuronal processes, dendrites and axons.
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19
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Murphy-Baum BL, Awatramani GB. Parallel processing in active dendrites during periods of intense spiking activity. Cell Rep 2022; 38:110412. [PMID: 35196499 DOI: 10.1016/j.celrep.2022.110412] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/15/2021] [Accepted: 01/28/2022] [Indexed: 12/17/2022] Open
Abstract
A neuron's ability to perform parallel computations throughout its dendritic arbor substantially improves its computational capacity. However, during natural patterns of activity, the degree to which computations remain compartmentalized, especially in neurons with active dendritic trees, is not clear. Here, we examine how the direction of moving objects is computed across the bistratified dendritic arbors of ON-OFF direction-selective ganglion cells (DSGCs) in the mouse retina. We find that although local synaptic signals propagate efficiently throughout their dendritic trees, direction-selective computations in one part of the dendritic arbor have little effect on those being made elsewhere. Independent dendritic processing allows DSGCs to compute the direction of moving objects multiple times as they traverse their receptive fields, enabling them to rapidly detect changes in motion direction on a sub-receptive-field basis. These results demonstrate that the parallel processing capacity of neurons can be maintained even during periods of intense synaptic activity.
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Affiliation(s)
| | - Gautam B Awatramani
- Department of Biology, University of Victoria, Victoria, BC V8P 5C2, Canada.
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20
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Larkum ME, Wu J, Duverdin SA, Gidon A. The guide to dendritic spikes of the mammalian cortex in vitro and in vivo. Neuroscience 2022; 489:15-33. [PMID: 35182699 DOI: 10.1016/j.neuroscience.2022.02.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 02/01/2022] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
Abstract
Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.
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Affiliation(s)
- Matthew E Larkum
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany; NeuroCure Cluster, Charité - Universitätsmedizin Berlin, Germany
| | - Jiameng Wu
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Berlin, Germany
| | - Sarah A Duverdin
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Integrative Neurophysiology, Amsterdam Neuroscience, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Albert Gidon
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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21
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Jia S, Xing D, Yu Z, Liu JK. Dissecting cascade computational components in spiking neural networks. PLoS Comput Biol 2021; 17:e1009640. [PMID: 34843460 PMCID: PMC8659421 DOI: 10.1371/journal.pcbi.1009640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 12/09/2021] [Accepted: 11/14/2021] [Indexed: 01/15/2023] Open
Abstract
Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity. It is well known that the computation of neuronal circuits is carried out through the staged and cascade structure of different types of neurons. Nevertheless, the information, particularly sensory information, is processed in a network primarily with feedforward connections through different pathways. A peculiar example is the early visual system, where light is transcoded by the retinal cells, routed by the lateral geniculate nucleus, and reached the primary visual cortex. One meticulous interest in recent years is to map out these physical structures of neuronal pathways. However, most methods so far are limited to taking snapshots of a static view of connections between neurons. It remains unclear how to obtain a functional and dynamical neuronal circuit beyond the simple scenarios of a few randomly sampled neurons. Using simulated spiking neural networks of visual pathways with different scenarios of multiple stages, mixed cell types, and natural image stimuli, we demonstrate that a recent computational tool, named spike-triggered non-negative matrix factorization, can resolve these issues. It enables us to recover the entire structural components of neural networks underlying the computation, together with the functional components of each individual neuron. Utilizing it for complex cells of the primary visual cortex allows us to reveal every underpinning of the nonlinear computation. Our results, together with other recent experimental and computational efforts, show that it is possible to systematically dissect neural circuitry with detailed structural and functional components.
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Affiliation(s)
- Shanshan Jia
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhaofei Yu
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
- * E-mail: (ZY); (JKL)
| | - Jian K. Liu
- School of Computing, University of Leeds, Leeds, United Kingdom
- * E-mail: (ZY); (JKL)
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22
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A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron 2021; 109:4001-4017.e10. [PMID: 34715026 PMCID: PMC8691952 DOI: 10.1016/j.neuron.2021.09.044] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/10/2021] [Accepted: 09/23/2021] [Indexed: 11/23/2022]
Abstract
Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.
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