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Chu T, Ji Z, Zuo J, Mi Y, Zhang WH, Huang T, Bush D, Burgess N, Wu S. Firing rate adaptation affords place cell theta sweeps, phase precession, and procession. eLife 2024; 12:RP87055. [PMID: 39037765 PMCID: PMC11262797 DOI: 10.7554/elife.87055] [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] [Indexed: 07/23/2024] Open
Abstract
Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between 'bimodal cells' showing interleaved phase precession and procession, and 'unimodal cells' in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells' firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.
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Affiliation(s)
- Tianhao Chu
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zilong Ji
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Institute of Cognitive Neuroscience, University College LondonLondonUnited Kingdom
| | - Junfeng Zuo
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Yuanyuan Mi
- Department of Psychology, Tsinghua UniversityBeijingChina
| | - Wen-hao Zhang
- Lyda Hill Department of Bioinformatics, O’Donnell Brain Institute, The University of Texas Southwestern Medical CenterDallasUnited States
| | - Tiejun Huang
- School of Computer Science, Peking UniversityBeijingChina
| | - Daniel Bush
- Department of Neuroscience, Physiology and Pharmacology, University College LondonLondonUnited Kingdom
| | - Neil Burgess
- Institute of Cognitive Neuroscience, University College LondonLondonUnited Kingdom
| | - Si Wu
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
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2
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Zhao H, Yang S, Fung CCA. Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors. Front Comput Neurosci 2023; 17:1231924. [PMID: 38024449 PMCID: PMC10652417 DOI: 10.3389/fncom.2023.1231924] [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: 06/05/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The N-methyl-D-aspartate receptor (NMDAR) plays a critical role in synaptic transmission and is associated with various neurological and psychiatric disorders. Recently, a novel form of postsynaptic plasticity known as NMDAR-based short-term postsynaptic plasticity (STPP) has been identified. It has been suggested that long-lasting glutamate binding to NMDAR allows for the retention of input information in brain slices up to 500 ms, leading to response facilitation. However, the impact of STPP on the dynamics of neuronal populations remains unexplored. Methods In this study, we incorporated STPP into a continuous attractor neural network (CANN) model to investigate its effects on neural information encoding in populations of neurons. Unlike short-term facilitation, a form of presynaptic plasticity, the temporally enhanced synaptic efficacy resulting from STPP destabilizes the network state of the CANN by increasing its mobility. Results Our findings demonstrate that the inclusion of STPP in the CANN model enables the network state to predictively respond to a moving stimulus. This nontrivial dynamical effect facilitates the tracking of the anticipated stimulus, as the enhanced synaptic efficacy induced by STPP enhances the system's mobility. Discussion The discovered STPP-based mechanism for sensory prediction provides valuable insights into the potential development of brain-inspired computational algorithms for prediction. By elucidating the role of STPP in neural population dynamics, this study expands our understanding of the functional implications of NMDAR-related plasticity in information processing within the brain. Conclusion The incorporation of STPP into a CANN model highlights its influence on the mobility and predictive capabilities of neural networks. These findings contribute to our knowledge of STPP-based mechanisms and their potential applications in developing computational algorithms for sensory prediction.
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Affiliation(s)
| | - Sungchil Yang
- Department of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Chi Chung Alan Fung
- Department of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong SAR, China
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3
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Yan M, Zhang WH, Wang H, Wong KYM. Bimodular continuous attractor neural networks with static and moving stimuli. Phys Rev E 2023; 107:064302. [PMID: 37464697 DOI: 10.1103/physreve.107.064302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/08/2023] [Indexed: 07/20/2023]
Abstract
We investigated the dynamical behaviors of bimodular continuous attractor neural networks, each processing a modality of sensory input and interacting with each other. We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory. When one intermodular coupling is excitatory while another is moderately inhibitory, temporally modulated population spikes can be generated. On further increase of the inhibitory coupling, momentary spikes will emerge. In the regime of bump coexistence, bump heights are primarily strengthened by excitatory intermodular couplings, but there is a lesser weakening effect due to a bump being displaced from the direct input. When bimodular networks serve as decoders of multisensory integration, we extend the Bayesian framework to show that excitatory and inhibitory couplings encode attractive and repulsive priors, respectively. At low disparity, the bump positions decode the posterior means in the Bayesian framework, whereas at high disparity, multiple steady states exist. In the regime of multiple steady states, the less stable state can be accessed if the input causing the more stable state arrives after a sufficiently long delay. When one input is moving, the bump in the corresponding module is pinned when the moving stimulus is weak, unpinned at intermediate stimulus strength, and tracks the input at strong stimulus strength, and the stimulus strengths for these transitions increase with the velocity of the moving stimulus. These results are important to understanding multisensory integration of static and dynamic stimuli.
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Affiliation(s)
- Min Yan
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China
| | - Wen-Hao Zhang
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- O'Donnell Brain Institute, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - He Wang
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China
- Hong Kong University of Science and Technology, Shenzhen Research Institute, Shenzhen 518057, China
| | - K Y Michael Wong
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China
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Lei L, Zhang M, Li T, Dong Y, Wang DH. A spiking network model for clustering report in a visual working memory task. Front Comput Neurosci 2023; 16:1030073. [PMID: 36714529 PMCID: PMC9878295 DOI: 10.3389/fncom.2022.1030073] [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: 09/15/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction Working memory (WM) plays a key role in many cognitive processes, and great interest has been attracted by WM for many decades. Recently, it has been observed that the reports of the memorized color sampled from a uniform distribution are clustered, and the report error for the stimulus follows a Gaussian distribution. Methods Based on the well-established ring model for visuospatial WM, we constructed a spiking network model with heterogeneous connectivity and embedded short-term plasticity (STP) to investigate the neurodynamic mechanisms behind this interesting phenomenon. Results As a result, our model reproduced the clustering report given stimuli sampled from a uniform distribution and the error of the report following a Gaussian distribution. Perturbation studies showed that the heterogeneity of connectivity and STP are necessary to explain experimental observations. Conclusion Our model provides a new perspective on the phenomenon of visual WM in experiments.
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Affiliation(s)
- Lixing Lei
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Mengya Zhang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Tingyu Li
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Yelin Dong
- School of Systems Science, Beijing Normal University, Beijing, China
- Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY, United States
| | - Da-Hui Wang
- School of Systems Science, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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An Efficient Population Density Method for Modeling Neural Networks with Synaptic Dynamics Manifesting Finite Relaxation Time and Short-Term Plasticity. eNeuro 2019; 5:eN-MNT-0002-18. [PMID: 30662939 PMCID: PMC6336402 DOI: 10.1523/eneuro.0002-18.2018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 10/24/2018] [Accepted: 11/21/2018] [Indexed: 12/05/2022] Open
Abstract
When incorporating more realistic synaptic dynamics, the computational efficiency of population density methods (PDMs) declines sharply due to the increase in the dimension of master equations. To avoid such a decline, we develop an efficient PDM, termed colored-synapse PDM (csPDM), in which the dimension of the master equations does not depend on the number of synapse-associated state variables in the underlying network model. Our goal is to allow the PDM to incorporate realistic synaptic dynamics that possesses not only finite relaxation time but also short-term plasticity (STP). The model equations of csPDM are derived based on the diffusion approximation on synaptic dynamics and probability density function methods for Langevin equations with colored noise. Numerical examples, given by simulations of the population dynamics of uncoupled exponential integrate-and-fire (EIF) neurons, show good agreement between the results of csPDM and Monte Carlo simulations (MCSs). Compared to the original full-dimensional PDM (fdPDM), the csPDM reveals more excellent computational efficiency because of the lower dimension of the master equations. In addition, it permits network dynamics to possess the short-term plastic characteristics inherited from plastic synapses. The novel csPDM has potential applicability to any spiking neuron models because of no assumptions on neuronal dynamics, and, more importantly, this is the first report of PDM to successfully encompass short-term facilitation/depression properties.
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6
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A neuro-inspired visual tracking method based on programmable system-on-chip platform. Neural Comput Appl 2018. [DOI: 10.1007/s00521-017-2847-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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7
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Activity dependent feedback inhibition may maintain head direction signals in mouse presubiculum. Nat Commun 2017; 8:16032. [PMID: 28726769 PMCID: PMC5524997 DOI: 10.1038/ncomms16032] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/17/2017] [Indexed: 11/09/2022] Open
Abstract
Orientation in space is represented in specialized brain circuits. Persistent head direction signals are transmitted from anterior thalamus to the presubiculum, but the identity of the presubicular target neurons, their connectivity and function in local microcircuits are unknown. Here, we examine how thalamic afferents recruit presubicular principal neurons and Martinotti interneurons, and the ensuing synaptic interactions between these cells. Pyramidal neuron activation of Martinotti cells in superficial layers is strongly facilitating such that high-frequency head directional stimulation efficiently unmutes synaptic excitation. Martinotti-cell feedback plays a dual role: precisely timed spikes may not inhibit the firing of in-tune head direction cells, while exerting lateral inhibition. Autonomous attractor dynamics emerge from a modelled network implementing wiring motifs and timing sensitive synaptic interactions in the pyramidal—Martinotti-cell feedback loop. This inhibitory microcircuit is therefore tuned to refine and maintain head direction information in the presubiculum. Head direction is encoded by cells in the presubiculum, but the role of local circuitry in head direction encoding remains unknown. Here the authors demonstrate how a specific inhibitory neuron type, the Martinotti cell, together with excitatory pyramidal cells supports head direction signals.
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Mark S, Romani S, Jezek K, Tsodyks M. Theta-paced flickering between place-cell maps in the hippocampus: A model based on short-term synaptic plasticity. Hippocampus 2017; 27:959-970. [PMID: 28558154 PMCID: PMC5575492 DOI: 10.1002/hipo.22743] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 05/16/2017] [Accepted: 05/18/2017] [Indexed: 01/29/2023]
Abstract
Hippocampal place cells represent different environments with distinct neural activity patterns. Following an abrupt switch between two familiar configurations of visual cues defining two environments, the hippocampal neural activity pattern switches almost immediately to the corresponding representation. Surprisingly, during a transient period following the switch to the new environment, occasional fast transitions between the two activity patterns (flickering) were observed (Jezek, Henriksen, Treves, Moser, & Moser, 2011). Here we show that an attractor neural network model of place cells with connections endowed with short‐term synaptic plasticity can account for this phenomenon. A memory trace of the recent history of network activity is maintained in the state of the synapses, allowing the network to temporarily reactivate the representation of the previous environment in the absence of the corresponding sensory cues. The model predicts that the number of flickering events depends on the amplitude of the ongoing theta rhythm and the distance between the current position of the animal and its position at the time of cue switching. We test these predictions with new analysis of experimental data. These results suggest a potential role of short‐term synaptic plasticity in recruiting the activity of different cell assemblies and in shaping hippocampal activity of behaving animals.
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Affiliation(s)
- Shirley Mark
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Sandro Romani
- HHMI Janelia Research Campus, Ashburn, Virginia, 20147, USA
| | - Karel Jezek
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, 32300, Czech Republic.,Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Misha Tsodyks
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, 76100, Israel
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9
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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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10
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Yuan WJ, Zhou JF, Zhou C. Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression. Phys Rev E 2016; 93:042302. [PMID: 27176307 DOI: 10.1103/physreve.93.042302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Indexed: 06/05/2023]
Abstract
Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.
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Affiliation(s)
- Wu-Jie Yuan
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | - Jian-Fang Zhou
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
| | - Changsong Zhou
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
- Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
- Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China
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11
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Wu S, Wong KYM, Fung CCA, Mi Y, Zhang W. Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation. F1000Res 2016; 5. [PMID: 26937278 PMCID: PMC4752021 DOI: 10.12688/f1000research.7387.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/04/2016] [Indexed: 11/30/2022] Open
Abstract
Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation.
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Affiliation(s)
- Si Wu
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - K Y Michael Wong
- Department of Physics, Hong Kong University of Science & Technology, Clear Water Bay Peninsula, Hong Kong
| | - C C Alan Fung
- RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
| | - Yuanyuan Mi
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Wenhao Zhang
- State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Department of Physics, Hong Kong University of Science & Technology, Clear Water Bay Peninsula, Hong Kong
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12
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Latorre R, Torres JJ, Varona P. Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons. PLoS One 2016; 11:e0145830. [PMID: 26730737 PMCID: PMC4701431 DOI: 10.1371/journal.pone.0145830] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 11/03/2015] [Indexed: 11/25/2022] Open
Abstract
In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations. Factors such as maximum hyperpolarization level, oscillation amplitude and frequency or the resulting firing threshold are modulated by synaptic depression, which can even make subthreshold oscillations disappear. This influence reshapes the postsynaptic neuron’s resonant properties arising from subthreshold oscillations and leads to specific input/output relations. We also study the neuron’s response to another simultaneous input in the context of this modulation, and show a distinct contextual processing as a function of the depression, in particular for detection of signals through weak synapses. Intrinsic oscillations dynamics can be combined with the characteristic time scale of the modulatory input received by a dynamic synapse to build cost-effective cell/channel-specific information discrimination mechanisms, beyond simple resonances. In this regard, we discuss the functional implications of synaptic depression modulation on intrinsic subthreshold dynamics.
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Affiliation(s)
- Roberto Latorre
- Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049, Madrid, Spain
- * E-mail:
| | - Joaquín J. Torres
- Departamento de Electromagnetismo y Física de la Materia, and Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain
| | - Pablo Varona
- Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049, Madrid, Spain
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13
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Wang H, Lam K, Fung CCA, Wong KYM, Wu S. Rich spectrum of neural field dynamics in the presence of short-term synaptic depression. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:032908. [PMID: 26465541 DOI: 10.1103/physreve.92.032908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Indexed: 06/05/2023]
Abstract
In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.
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Affiliation(s)
- He Wang
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
| | - Kin Lam
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
| | - C C Alan Fung
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
| | - K Y Michael Wong
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
| | - Si Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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14
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Fung CCA, Wong KYM, Mao H, Wu S. Fluctuation-response relation unifies dynamical behaviors in neural fields. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:022801. [PMID: 26382448 DOI: 10.1103/physreve.92.022801] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Indexed: 06/05/2023]
Abstract
Anticipation is a strategy used by neural fields to compensate for transmission and processing delays during the tracking of dynamical information and can be achieved by slow, localized, inhibitory feedback mechanisms such as short-term synaptic depression, spike-frequency adaptation, or inhibitory feedback from other layers. Based on the translational symmetry of the mobile network states, we derive generic fluctuation-response relations, providing unified predictions that link their tracking behaviors in the presence of external stimuli to the intrinsic dynamics of the neural fields in their absence.
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Affiliation(s)
- C C Alan Fung
- Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - K Y Michael Wong
- Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Hongzi Mao
- Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Si Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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15
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Page HJI, Walters D, Stringer SM. Architectural constraints are a major factor reducing path integration accuracy in the rat head direction cell system. Front Comput Neurosci 2015; 9:10. [PMID: 25705190 PMCID: PMC4319401 DOI: 10.3389/fncom.2015.00010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Accepted: 01/18/2015] [Indexed: 11/13/2022] Open
Abstract
Head direction cells fire to signal the direction in which an animal's head is pointing. They are able to track head direction using only internally-derived information (path integration)In this simulation study we investigate the factors that affect path integration accuracy. Specifically, two major limiting factors are identified: rise time, the time after stimulation it takes for a neuron to start firing, and the presence of symmetric non-offset within-layer recurrent collateral connectivity. On the basis of the latter, the important prediction is made that head direction cell regions directly involved in path integration will not contain this type of connectivity; giving a theoretical explanation for architectural observations. Increased neuronal rise time is found to slow path integration, and the slowing effect for a given rise time is found to be more severe in the context of short conduction delays. Further work is suggested on the basis of our findings, which represent a valuable contribution to understanding of the head direction cell system.
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Affiliation(s)
- Hector J I Page
- Departmental of Experimental Psychology, Oxford Center for Theoretical Neuroscience and Artificial Intelligence, University of Oxford Oxford, UK
| | - Daniel Walters
- Departmental of Experimental Psychology, Oxford Center for Theoretical Neuroscience and Artificial Intelligence, University of Oxford Oxford, UK
| | - Simon M Stringer
- Departmental of Experimental Psychology, Oxford Center for Theoretical Neuroscience and Artificial Intelligence, University of Oxford Oxford, UK
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16
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Abstract
Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) describe the neural processing of continuous information such as object position, object orientation, and direction of object motion. Recently it was found that in one-dimensional CANNs, short-term synaptic depression can destabilize bump-shaped neuronal attractor activity profiles. In this article, we study two-dimensional CANNs with short-term synaptic depression and spike frequency adaptation. We found that the dynamics of CANNs with short-term synaptic depression and CANNs with spike frequency adaptation are qualitatively similar. We also found that in both kinds of CANNs, the perturbative approach can be used to predict phase diagrams, dynamical variables, and speed of spontaneous motion.
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Affiliation(s)
- C C Alan Fung
- Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
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Monasson R, Rosay S. Crosstalk and transitions between multiple spatial maps in an attractor neural network model of the hippocampus: collective motion of the activity. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 89:032803. [PMID: 24730895 DOI: 10.1103/physreve.89.032803] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Indexed: 06/03/2023]
Abstract
The dynamics of a neural model for hippocampal place cells storing spatial maps is studied. In the absence of external input, depending on the number of cells and on the values of control parameters (number of environments stored, level of neural noise, average level of activity, connectivity of place cells), a "clump" of spatially localized activity can diffuse or remains pinned due to crosstalk between the environments. In the single-environment case, the macroscopic coefficient of diffusion of the clump and its effective mobility are calculated analytically from first principles and corroborated by numerical simulations. In the multienvironment case the heights and the widths of the pinning barriers are analytically characterized with the replica method; diffusion within one map is then in competition with transitions between different maps. Possible mechanisms enhancing mobility are proposed and tested.
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Affiliation(s)
- R Monasson
- Laboratoire de Physique Théorique de l'ENS, CNRS & UPMC, 24 rue Lhomond, 75005 Paris, France
| | - S Rosay
- Laboratoire de Physique Théorique de l'ENS, CNRS & UPMC, 24 rue Lhomond, 75005 Paris, France
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Fung CCA, Wang H, Lam K, Wong KYM, Wu S. Resolution enhancement in neural networks with dynamical synapses. Front Comput Neurosci 2013; 7:73. [PMID: 23781197 PMCID: PMC3677988 DOI: 10.3389/fncom.2013.00073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2013] [Accepted: 05/15/2013] [Indexed: 11/29/2022] Open
Abstract
Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.
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Affiliation(s)
- C C Alan Fung
- Department of Physics, The Hong Kong University of Science and Technology Hong Kong, China
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Costa RP, Sjöström PJ, van Rossum MCW. Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits. Front Comput Neurosci 2013; 7:75. [PMID: 23761760 PMCID: PMC3674479 DOI: 10.3389/fncom.2013.00075] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2013] [Accepted: 05/17/2013] [Indexed: 11/25/2022] Open
Abstract
Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.
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Affiliation(s)
- Rui P Costa
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh Edinburgh, UK
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Scott P, Cowan AI, Stricker C. Quantifying impacts of short-term plasticity on neuronal information transfer. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 85:041921. [PMID: 22680512 DOI: 10.1103/physreve.85.041921] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Revised: 02/09/2012] [Indexed: 06/01/2023]
Abstract
Short-term changes in efficacy have been postulated to enhance the ability of synapses to transmit information between neurons, and within neuronal networks. Even at the level of connections between single neurons, direct confirmation of this simple conjecture has proven elusive. By combining paired-cell recordings, realistic synaptic modeling, and information theory, we provide evidence that short-term plasticity can not only improve, but also reduce information transfer between neurons. We focus on a concrete example in rat neocortex, but our results may generalize to other systems. When information is contained in the timings of individual spikes, we find that facilitation, depression, and recovery affect information transmission in proportion to their impacts upon the probability of neurotransmitter release. When information is instead conveyed by mean spike rate only, the influences of short-term plasticity critically depend on the range of spike frequencies that the target network can distinguish (its effective dynamic range). Our results suggest that to efficiently transmit information, the brain must match synaptic type, coding strategy, and network connectivity during development and behavior.
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Affiliation(s)
- Pat Scott
- Department of Physics, McGill University, 3600 rue University, Montréal, Canada, QC H3A 2T8.
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