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Nelson AD, Catalfio AM, Gupta JP, Min L, Caballero-Florán RN, Dean KP, Elvira CC, Derderian KD, Kyoung H, Sahagun A, Sanders SJ, Bender KJ, Jenkins PM. Physical and functional convergence of the autism risk genes Scn2a and Ank2 in neocortical pyramidal cell dendrites. Neuron 2024; 112:1133-1149.e6. [PMID: 38290518 PMCID: PMC11097922 DOI: 10.1016/j.neuron.2024.01.003] [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: 06/08/2022] [Revised: 04/26/2023] [Accepted: 01/03/2024] [Indexed: 02/01/2024]
Abstract
Dysfunction in sodium channels and their ankyrin scaffolding partners have both been implicated in neurodevelopmental disorders, including autism spectrum disorder (ASD). In particular, the genes SCN2A, which encodes the sodium channel NaV1.2, and ANK2, which encodes ankyrin-B, have strong ASD association. Recent studies indicate that ASD-associated haploinsufficiency in Scn2a impairs dendritic excitability and synaptic function in neocortical pyramidal cells, but how NaV1.2 is anchored within dendritic regions is unknown. Here, we show that ankyrin-B is essential for scaffolding NaV1.2 to the dendritic membrane of mouse neocortical neurons and that haploinsufficiency of Ank2 phenocopies intrinsic dendritic excitability and synaptic deficits observed in Scn2a+/- conditions. These results establish a direct, convergent link between two major ASD risk genes and reinforce an emerging framework suggesting that neocortical pyramidal cell dendritic dysfunction can contribute to neurodevelopmental disorder pathophysiology.
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Affiliation(s)
- Andrew D Nelson
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Amanda M Catalfio
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Julie P Gupta
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lia Min
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Kendall P Dean
- Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Carina C Elvira
- Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kimberly D Derderian
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Henry Kyoung
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Atehsa Sahagun
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Stephan J Sanders
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Kevin J Bender
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Paul M Jenkins
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
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2
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Chen G, Zhang Y, Li X, Zhao X, Ye Q, Lin Y, Tao HW, Rasch MJ, Zhang X. Distinct Inhibitory Circuits Orchestrate Cortical beta and gamma Band Oscillations. Neuron 2019; 96:1403-1418.e6. [PMID: 29268099 DOI: 10.1016/j.neuron.2017.11.033] [Citation(s) in RCA: 175] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 09/25/2017] [Accepted: 11/20/2017] [Indexed: 12/29/2022]
Abstract
Distinct subtypes of inhibitory interneuron are known to shape diverse rhythmic activities in the cortex, but how they interact to orchestrate specific band activity remains largely unknown. By recording optogenetically tagged interneurons of specific subtypes in the primary visual cortex of behaving mice, we show that spiking of somatostatin (SOM)- and parvalbumin (PV)-expressing interneurons preferentially correlates with cortical beta and gamma band oscillations, respectively. Suppression of SOM cell spiking reduces the spontaneous low-frequency band (<30-Hz) oscillations and selectively reduces visually induced enhancement of beta oscillation. In comparison, suppressing PV cell activity elevates the synchronization of spontaneous activity across a broad frequency range and further precludes visually induced changes in beta and gamma oscillations. Rhythmic activation of SOM and PV cells in the local circuit entrains resonant activity in the narrow 5- to 30-Hz band and the wide 20- to 80-Hz band, respectively. Together, these findings reveal differential and cooperative roles of SOM and PV inhibitory neurons in orchestrating specific cortical oscillations.
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Affiliation(s)
- Guang Chen
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuan Zhang
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiang Li
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaochen Zhao
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qian Ye
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yingxi Lin
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Huizhong W Tao
- Zilkha Neurogenetic Institute, Department of Cell and Neurobiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Malte J Rasch
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Xiaohui Zhang
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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3
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D'Onofrio G, Tamborrino M, Lansky P. The Jacobi diffusion process as a neuronal model. CHAOS (WOODBURY, N.Y.) 2018; 28:103119. [PMID: 30384666 DOI: 10.1063/1.5051494] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 10/01/2018] [Indexed: 06/08/2023]
Abstract
The Jacobi process is a stochastic diffusion characterized by a linear drift and a special form of multiplicative noise which keeps the process confined between two boundaries. One example of such a process can be obtained as the diffusion limit of the Stein's model of membrane depolarization which includes both excitatory and inhibitory reversal potentials. The reversal potentials create the two boundaries between which the process is confined. Solving the first-passage-time problem for the Jacobi process, we found closed-form expressions for mean, variance, and third moment that are easy to implement numerically. The first two moments are used here to determine the role played by the parameters of the neuronal model; namely, the effect of multiplicative noise on the output of the Jacobi neuronal model with input-dependent parameters is examined in detail and compared with the properties of the generic Jacobi diffusion. It appears that the dependence of the model parameters on the rate of inhibition turns out to be of primary importance to observe a change in the slope of the response curves. This dependence also affects the variability of the output as reflected by the coefficient of variation. It often takes values larger than one, and it is not always a monotonic function in dependency on the rate of excitation.
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Affiliation(s)
- Giuseppe D'Onofrio
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic
| | - Massimiliano Tamborrino
- Johannes Kepler University Linz, Institute for Stochastics Altenbergerstraße 69, 4040 Linz, Austria
| | - Petr Lansky
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic
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4
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Rutishauser U, Slotine JJ, Douglas RJ. Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks. Neural Comput 2018; 30:1359-1393. [PMID: 29566357 PMCID: PMC5930080 DOI: 10.1162/neco_a_01074] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSP's planar four-color graph coloring, maximum independent set, and sudoku on this substrate and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of nonsaturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by nonlinear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation and offer insight into the computational role of dual inhibitory mechanisms in neural circuits.
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Affiliation(s)
- Ueli Rutishauser
- Computation and Neural Systems, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, U.S.A., and Cedars-Sinai Medical Center, Departments of Neurosurgery, Neurology and Biomedical Sciences, Los Angeles, CA 90048, U.S.A.
| | - Jean-Jacques Slotine
- Nonlinear Systems Laboratory, Department of Mechanical Engineering and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A.
| | - Rodney J Douglas
- Institute of Neuroinformatics, University and ETH Zurich, Zurich 8057, Switzerland
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5
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Turco CV, El-Sayes J, Savoie MJ, Fassett HJ, Locke MB, Nelson AJ. Short- and long-latency afferent inhibition; uses, mechanisms and influencing factors. Brain Stimul 2018; 11:59-74. [DOI: 10.1016/j.brs.2017.09.009] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/28/2017] [Accepted: 09/14/2017] [Indexed: 12/11/2022] Open
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Aspart F, Ladenbauer J, Obermayer K. Extending Integrate-and-Fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite. PLoS Comput Biol 2016; 12:e1005206. [PMID: 27893786 PMCID: PMC5125569 DOI: 10.1371/journal.pcbi.1005206] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 10/17/2016] [Indexed: 12/31/2022] Open
Abstract
Transcranial brain stimulation and evidence of ephaptic coupling have recently sparked strong interests in understanding the effects of weak electric fields on the dynamics of brain networks and of coupled populations of neurons. The collective dynamics of large neuronal populations can be efficiently studied using single-compartment (point) model neurons of the integrate-and-fire (IF) type as their elements. These models, however, lack the dendritic morphology required to biophysically describe the effect of an extracellular electric field on the neuronal membrane voltage. Here, we extend the IF point neuron models to accurately reflect morphology dependent electric field effects extracted from a canonical spatial “ball-and-stick” (BS) neuron model. Even in the absence of an extracellular field, neuronal morphology by itself strongly affects the cellular response properties. We, therefore, derive additional components for leaky and nonlinear IF neuron models to reproduce the subthreshold voltage and spiking dynamics of the BS model exposed to both fluctuating somatic and dendritic inputs and an extracellular electric field. We show that an oscillatory electric field causes spike rate resonance, or equivalently, pronounced spike to field coherence. Its resonance frequency depends on the location of the synaptic background inputs. For somatic inputs the resonance appears in the beta and gamma frequency range, whereas for distal dendritic inputs it is shifted to even higher frequencies. Irrespective of an external electric field, the presence of a dendritic cable attenuates the subthreshold response at the soma to slowly-varying somatic inputs while implementing a low-pass filter for distal dendritic inputs. Our point neuron model extension is straightforward to implement and is computationally much more efficient compared to the original BS model. It is well suited for studying the dynamics of large populations of neurons with heterogeneous dendritic morphology with (and without) the influence of weak external electric fields. How extracellular electric fields—as generated endogenously or through transcranial brain stimulation—affect the dynamics of neuronal populations is of great interest but not well understood. To study neuronal activity at the network level single-compartment neuron models have been proven very successful, because of their computational efficiency and analytical tractability. Unfortunately, these models lack the dendritic morphology to biophysically account for the effects of electric fields, and for changes in synaptic integration due to morphology alone. Here, we consider a canonical, spatially extended model neuron and characterize its responses to fluctuating synaptic input as well as an oscillatory, weak electric field. In order to accurately reproduce these responses we analytically derive an extension for the popular integrate-and-fire point neuron models. We show that the dendritic cable acts as a filter for the synaptic input current, which depends on the input location, and that an electric field modulates the neuronal spike rate strongest at a certain (preferred) field frequency. These phenomena can be successfully reproduced using integrate-and-fire models, extended by a small number of components that are straightforward to implement. The extended point models are thus well suited for studying populations of coupled neurons with different morphology, exposed to extracellular electric fields.
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Affiliation(s)
- Florian Aspart
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- * E-mail: (FA); (JL); (KO)
| | - Josef Ladenbauer
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- * E-mail: (FA); (JL); (KO)
| | - Klaus Obermayer
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- * E-mail: (FA); (JL); (KO)
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7
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Mi Y, Lin X, Wu S. Neural Computations in a Dynamical System with Multiple Time Scales. Front Comput Neurosci 2016; 10:96. [PMID: 27679569 PMCID: PMC5020071 DOI: 10.3389/fncom.2016.00096] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 08/25/2016] [Indexed: 11/13/2022] Open
Abstract
Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.
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Affiliation(s)
- Yuanyuan Mi
- Brain Science Center, Institute of Basic Medical SciencesBeijing, China; State Key Lab of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China
| | - Xiaohan Lin
- State Key Lab of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Si Wu
- State Key Lab of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
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8
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Cash RFH, Isayama R, Gunraj CA, Ni Z, Chen R. The influence of sensory afferent input on local motor cortical excitatory circuitry in humans. J Physiol 2015; 593:1667-84. [PMID: 25832926 PMCID: PMC4386965 DOI: 10.1113/jphysiol.2014.286245] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Accepted: 12/21/2014] [Indexed: 11/08/2022] Open
Abstract
In human, sensorimotor integration can be investigated by combining sensory input and transcranial magnetic stimulation (TMS). Short latency afferent inhibition (SAI) refers to motor cortical inhibition 20-25 ms after median nerve stimulation. We investigated the interaction between SAI and short-interval intracortical facilitation (SICF), an excitatory motor cortical circuit. Seven experiments were performed. Contrary to expectations, SICF was facilitated in the presence of SAI (SICF(SAI)). This effect is specific to SICF since there was no effect at SICF trough 1 when SICF was absent. Furthermore, the facilitatory SICF(SAI) interaction increased with stronger SICF or SAI. SAI and SICF correlated between individuals, and this relationship was maintained when SICF was delivered in the presence of SAI, suggesting an intrinsic relationship between SAI and SICF in sensorimotor integration. The interaction was present at rest and during muscle contraction, had a broad degree of somatotopic influence and was present in different interneuronal SICF circuits induced by posterior-anterior and anterior-posterior current directions. Our results are compatible with the finding that projections from sensory to motor cortex terminate in both superficial layers where late indirect (I-) waves are thought to originate, as well as deeper layers with more direct effect on pyramidal output. This interaction is likely to be relevant to sensorimotor integration and motor control.
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Affiliation(s)
- Robin F H Cash
- Division of Neurology, Department of Medicine, University of Toronto, Division of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, University Health NetworkToronto, Ontario, Canada
| | - Reina Isayama
- Division of Neurology, Department of Medicine, University of Toronto, Division of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, University Health NetworkToronto, Ontario, Canada
| | - Carolyn A Gunraj
- Division of Neurology, Department of Medicine, University of Toronto, Division of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, University Health NetworkToronto, Ontario, Canada
| | - Zhen Ni
- Division of Neurology, Department of Medicine, University of Toronto, Division of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, University Health NetworkToronto, Ontario, Canada
| | - Robert Chen
- Division of Neurology, Department of Medicine, University of Toronto, Division of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, University Health NetworkToronto, Ontario, Canada
- Corresponding author R. Chen: 13MP-304, 399 Bathurst Street, Toronto, Ontario, M5T 2S8, Canada.
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9
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Zhang D, Wu S, Rasch MJ. Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity. PLoS One 2015; 10:e0118125. [PMID: 25723493 PMCID: PMC4344245 DOI: 10.1371/journal.pone.0118125] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 01/06/2015] [Indexed: 01/26/2023] Open
Abstract
In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.
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Affiliation(s)
- Danke Zhang
- Department of Biomedical Engineering, Hangzhou Dianzi University, Hangzhou, China
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Si Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Malte J. Rasch
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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10
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Lazar AA, Zhou Y. Volterra dendritic stimulus processors and biophysical spike generators with intrinsic noise sources. Front Comput Neurosci 2014; 8:95. [PMID: 25225477 PMCID: PMC4150400 DOI: 10.3389/fncom.2014.00095] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Accepted: 07/23/2014] [Indexed: 11/13/2022] Open
Abstract
We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model in these circuits consists of a dendritic stimulus processor (DSP) cascaded with a biophysical spike generator (BSG). The dendritic stimulus processor is modeled as a set of nonlinear operators that are assumed to have a Volterra series representation. Biophysical point neuron models, such as the Hodgkin-Huxley neuron, are used to model the spike generator. We address the question of how intrinsic noise sources affect the precision in encoding and decoding of sensory stimuli and the functional identification of its sensory circuits. We investigate two intrinsic noise sources arising (i) in the active dendritic trees underlying the DSPs, and (ii) in the ion channels of the BSGs. Noise in dendritic stimulus processing arises from a combined effect of variability in synaptic transmission and dendritic interactions. Channel noise arises in the BSGs due to the fluctuation of the number of the active ion channels. Using a stochastic differential equations formalism we show that encoding with a neuron model consisting of a nonlinear DSP cascaded with a BSG with intrinsic noise sources can be treated as generalized sampling with noisy measurements. For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding. Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given. We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.
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Affiliation(s)
- Aurel A Lazar
- Department of Electrical Engineering, Columbia University New York, NY, USA
| | - Yiyin Zhou
- Department of Electrical Engineering, Columbia University New York, NY, USA
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11
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Hutt A, Buhry L. Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia. J Comput Neurosci 2014; 37:417-37. [PMID: 24976146 PMCID: PMC4224752 DOI: 10.1007/s10827-014-0512-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Revised: 06/06/2014] [Accepted: 06/10/2014] [Indexed: 01/22/2023]
Abstract
Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.
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Affiliation(s)
- Axel Hutt
- INRIA Grand Est - Nancy, Team NEUROSYS, 615 rue du Jardin Botanique, 54602 Villers-les-Nancy, France
| | - Laure Buhry
- INRIA Grand Est - Nancy, Team NEUROSYS, 615 rue du Jardin Botanique, 54602 Villers-les-Nancy, France
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