1
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Kim J, Bustamante E, Sotonyi P, Maxwell N, Parameswaran P, Kent JK, Wetsel WC, Soderblom EJ, Rácz B, Soderling SH. Presynaptic Rac1 in the hippocampus selectively regulates working memory. eLife 2024; 13:RP97289. [PMID: 39046788 PMCID: PMC11268886 DOI: 10.7554/elife.97289] [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: 07/25/2024] Open
Abstract
One of the most extensively studied members of the Ras superfamily of small GTPases, Rac1 is an intracellular signal transducer that remodels actin and phosphorylation signaling networks. Previous studies have shown that Rac1-mediated signaling is associated with hippocampal-dependent working memory and longer-term forms of learning and memory and that Rac1 can modulate forms of both pre- and postsynaptic plasticity. How these different cognitive functions and forms of plasticity mediated by Rac1 are linked, however, is unclear. Here, we show that spatial working memory in mice is selectively impaired following the expression of a genetically encoded Rac1 inhibitor at presynaptic terminals, while longer-term cognitive processes are affected by Rac1 inhibition at postsynaptic sites. To investigate the regulatory mechanisms of this presynaptic process, we leveraged new advances in mass spectrometry to identify the proteomic and post-translational landscape of presynaptic Rac1 signaling. We identified serine/threonine kinases and phosphorylated cytoskeletal signaling and synaptic vesicle proteins enriched with active Rac1. The phosphorylated sites in these proteins are at positions likely to have regulatory effects on synaptic vesicles. Consistent with this, we also report changes in the distribution and morphology of synaptic vesicles and in postsynaptic ultrastructure following presynaptic Rac1 inhibition. Overall, this study reveals a previously unrecognized presynaptic role of Rac1 signaling in cognitive processes and provides insights into its potential regulatory mechanisms.
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Affiliation(s)
- Jaebin Kim
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
| | - Edwin Bustamante
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
| | - Peter Sotonyi
- Department of Anatomy and Histology, University of Veterinary MedicineBudapestHungary
| | - Nicholas Maxwell
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
| | - Pooja Parameswaran
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
| | - Julie K Kent
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
| | - William C Wetsel
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
- Department of Psychiatry and Behavioral Sciences, Duke University School of MedicineDurhamUnited States
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University School of MedicineDurhamUnited States
- Department of Neurobiology, Duke University School of MedicineDurhamUnited States
| | - Erik J Soderblom
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
- Proteomics and Metabolomics Shared Resource and Center for Genomic and Computational Biology, Duke University School of MedicineDurhamUnited States
| | - Bence Rácz
- Department of Anatomy and Histology, University of Veterinary MedicineBudapestHungary
| | - Scott H Soderling
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
- Department of Neurobiology, Duke University School of MedicineDurhamUnited States
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2
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Zhou J, Gong L, Huang X, Mu C, Mi Y. The synaptic correlates of serial position effects in sequential working memory. Front Comput Neurosci 2024; 18:1430244. [PMID: 39077153 PMCID: PMC11284078 DOI: 10.3389/fncom.2024.1430244] [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: 05/09/2024] [Accepted: 06/26/2024] [Indexed: 07/31/2024] Open
Abstract
Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanism underpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, our model reproduces the the experimental finding that as the maintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM.
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Affiliation(s)
- Jiaqi Zhou
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Liping Gong
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Xiaodong Huang
- Department of Physics, South China University of Technology, Guangzhou, China
| | - Chunlai Mu
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Yuanyuan Mi
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
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3
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Mahrach A, Bestue D, Qi XL, Constantinidis C, Compte A. Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory. J Neurosci 2024; 44:e1225232024. [PMID: 38641409 PMCID: PMC11154852 DOI: 10.1523/jneurosci.1225-23.2024] [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: 06/30/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/21/2024] Open
Abstract
The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.
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Affiliation(s)
- Alexandre Mahrach
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | - David Bestue
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | - Xue-Lian Qi
- Wake Forest School of Medicine, Winston-Salem, North Carolina 27157
| | | | - Albert Compte
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
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4
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Kim J, Bustamante E, Sotonyi P, Maxwell ND, Parameswaran P, Kent JK, Wetsel WC, Soderblom EJ, Rácz B, Soderling SH. Presynaptic Rac1 in the hippocampus selectively regulates working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585488. [PMID: 38562715 PMCID: PMC10983896 DOI: 10.1101/2024.03.18.585488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
One of the most extensively studied members of the Ras superfamily of small GTPases, Rac1 is an intracellular signal transducer that remodels actin and phosphorylation signaling networks. Previous studies have shown that Rac1-mediated signaling is associated with hippocampal-dependent working memory and longer-term forms of learning and memory and that Rac1 can modulate forms of both pre- and postsynaptic plasticity. How these different cognitive functions and forms of plasticity mediated by Rac1 are linked, however, is unclear. Here, we show that spatial working memory is selectively impaired following the expression of a genetically encoded Rac1-inhibitor at presynaptic terminals, while longer-term cognitive processes are affected by Rac1 inhibition at postsynaptic sites. To investigate the regulatory mechanisms of this presynaptic process, we leveraged new advances in mass spectrometry to identify the proteomic and post-translational landscape of presynaptic Rac1 signaling. We identified serine/threonine kinases and phosphorylated cytoskeletal signaling and synaptic vesicle proteins enriched with active Rac1. The phosphorylated sites in these proteins are at positions likely to have regulatory effects on synaptic vesicles. Consistent with this, we also report changes in the distribution and morphology of synaptic vesicles and in postsynaptic ultrastructure following presynaptic Rac1 inhibition. Overall, this study reveals a previously unrecognized presynaptic role of Rac1 signaling in cognitive processes and provides insights into its potential regulatory mechanisms.
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Affiliation(s)
- Jaebin Kim
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
| | - Edwin Bustamante
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
| | - Peter Sotonyi
- Department of Anatomy and Histology, University of Veterinary Medicine, Budapest, Hungary
| | - Nicholas D Maxwell
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
| | - Pooja Parameswaran
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
| | - Julie K Kent
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
| | - William C Wetsel
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School, Durham, North Carolina, USA
- Department of Neurobiology, Duke University Medical School, Durham, North Carolina, USA
| | - Erik J Soderblom
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School, Durham, North Carolina, USA
| | - Bence Rácz
- Department of Anatomy and Histology, University of Veterinary Medicine, Budapest, Hungary
| | - Scott H Soderling
- Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA
- Department of Neurobiology, Duke University Medical School, Durham, North Carolina, USA
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5
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Ji X, Elmoznino E, Deane G, Constant A, Dumas G, Lajoie G, Simon J, Bengio Y. Sources of richness and ineffability for phenomenally conscious states. Neurosci Conscious 2024; 2024:niae001. [PMID: 38487679 PMCID: PMC10939345 DOI: 10.1093/nc/niae001] [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: 05/01/2023] [Revised: 01/03/2024] [Accepted: 01/23/2024] [Indexed: 03/17/2024] Open
Abstract
Conscious states-state that there is something it is like to be in-seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience-two important aspects that seem to be part of what makes qualitative character so puzzling.
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Affiliation(s)
- Xu Ji
- Mila - Quebec AI Institute, Montreal, Quebec H2S 3H1, Canada
- Department of Computer science and operations Research, University of Montreal, Pavillon André-Aisenstadt 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
| | - Eric Elmoznino
- Mila - Quebec AI Institute, Montreal, Quebec H2S 3H1, Canada
- Department of Computer science and operations Research, University of Montreal, Pavillon André-Aisenstadt 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
| | - George Deane
- Department of Philosophy, University of Montreal, Pavillon 2910, boul. Édouard-Montpetit, Montreal, Quebec H3C 3J7, Canada
| | - Axel Constant
- School of Engineering and Informatics, University of Sussex, Sussex House, Falmer, East Sussex BN1 9RH, United Kingdom
| | - Guillaume Dumas
- Mila - Quebec AI Institute, Montreal, Quebec H2S 3H1, Canada
- Department of Psychiatry and Addiction, University of Montreal, Pavillon Roger-Gaudry 2900, boul. Édouard-Montpetit, Montreal, Quebec H3T 1J4, Canada
| | - Guillaume Lajoie
- Mila - Quebec AI Institute, Montreal, Quebec H2S 3H1, Canada
- Department of Mathematics and Statistics, University of Montreal, Pavillon André-Aisenstadt (AA-5190) 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
| | - Jonathan Simon
- Department of Philosophy, University of Montreal, Pavillon 2910, boul. Édouard-Montpetit, Montreal, Quebec H3C 3J7, Canada
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec H2S 3H1, Canada
- Department of Computer science and operations Research, University of Montreal, Pavillon André-Aisenstadt 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
- CIFAR - Canadian Institute for Advanced Research, MaRS Centre, West Tower 661 University Ave., Suite 505, Toronto, Ontario M5G 1M1, Canada
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6
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Mahrach A, Bestue D, Qi XL, Constantinidis C, Compte A. Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576071. [PMID: 38293215 PMCID: PMC10827212 DOI: 10.1101/2024.01.17.576071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC.
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Affiliation(s)
- Alexandre Mahrach
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - David Bestue
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Xue-Lian Qi
- Wake Forest School of Medicine, Winston Salem, NC 27157, USA
| | | | - Albert Compte
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
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7
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Eissa TL, Kilpatrick ZP. Learning efficient representations of environmental priors in working memory. PLoS Comput Biol 2023; 19:e1011622. [PMID: 37943956 PMCID: PMC10662764 DOI: 10.1371/journal.pcbi.1011622] [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: 09/22/2022] [Revised: 11/21/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer's estimate of the environmental prior. For instance, when retaining an estimate of an object's features in working memory, learned priors may bias the estimate in the direction of common feature values. Humans display such biases when retaining color estimates on short time intervals. We propose that these systematic biases emerge from modulation of synaptic connectivity in a neural circuit based on the experienced stimulus history, shaping the persistent and collective neural activity that encodes the stimulus estimate. Resulting neural activity attractors are aligned to common stimulus values. Using recently published human response data from a delayed-estimation task in which stimuli (colors) were drawn from a heterogeneous distribution that did not necessarily correspond with reported population biases, we confirm that most subjects' response distributions are better described by experience-dependent learning models than by models with fixed biases. This work suggests systematic limitations in working memory reflect efficient representations of inferred environmental structure, providing new insights into how humans integrate environmental knowledge into their cognitive strategies.
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Affiliation(s)
- Tahra L. Eissa
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Zachary P. Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, Colorado, United States of America
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8
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Brennan C, Proekt A. Attractor dynamics with activity-dependent plasticity capture human working memory across time scales. COMMUNICATIONS PSYCHOLOGY 2023; 1:28. [PMID: 38764555 PMCID: PMC11101211 DOI: 10.1038/s44271-023-00027-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/15/2023] [Indexed: 05/21/2024]
Abstract
Most cognitive functions require the brain to maintain immediately preceding stimuli in working memory. Here, using a human working memory task with multiple delays, we test the hypothesis that working memories are stored in a discrete set of stable neuronal activity configurations called attractors. We show that while discrete attractor dynamics can approximate working memory on a single time scale, they fail to generalize across multiple timescales. This failure occurs because at longer delay intervals the responses contain more information about the stimuli than can be stored in a discrete attractor model. We present a modeling approach that combines discrete attractor dynamics with activity-dependent plasticity. This model successfully generalizes across all timescales and correctly predicts intertrial interactions. Thus, our findings suggest that discrete attractor dynamics are insufficient to model working memory and that activity-dependent plasticity improves durability of information storage in attractor systems.
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Affiliation(s)
- Connor Brennan
- University of Pennsylvania, 3160 Chestnut St., Philadelphia, PA USA
| | - Alex Proekt
- University of Pennsylvania, 3160 Chestnut St., Philadelphia, PA USA
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9
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Ma H, Qi Y, Gong P, Zhang J, Lu WL, Feng J. Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes. Neural Comput 2023; 35:1820-1849. [PMID: 37725705 DOI: 10.1162/neco_a_01612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/26/2023] [Indexed: 09/21/2023]
Abstract
Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.
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Affiliation(s)
- Hengyuan Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yang Qi
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW 2006, Australia
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Wen-Lian Lu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
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10
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Ye L, Feng J, Li C. Controlling brain dynamics: Landscape and transition path for working memory. PLoS Comput Biol 2023; 19:e1011446. [PMID: 37669311 PMCID: PMC10503743 DOI: 10.1371/journal.pcbi.1011446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/15/2023] [Accepted: 08/21/2023] [Indexed: 09/07/2023] Open
Abstract
Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.
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Affiliation(s)
- Leijun Ye
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- School of Mathematical Sciences and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
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11
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Hutt A, Rich S, Valiante TA, Lefebvre J. Intrinsic neural diversity quenches the dynamic volatility of neural networks. Proc Natl Acad Sci U S A 2023; 120:e2218841120. [PMID: 37399421 PMCID: PMC10334753 DOI: 10.1073/pnas.2218841120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/19/2023] [Indexed: 07/05/2023] Open
Abstract
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
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Affiliation(s)
- Axel Hutt
- Université de Strasbourg, CNRS, Inria, ICube, MLMS, MIMESIS, StrasbourgF-67000, France
| | - Scott Rich
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
| | - Taufik A. Valiante
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ONM5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ONM5S 3G9, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ONM5S 1A8, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ONM5G 2C4, Canada
- Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ONM5G 2A2, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ONM5S 3G8, Canada
| | - Jérémie Lefebvre
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, ONK1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
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12
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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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13
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Bachschmid-Romano L, Hatsopoulos NG, Brunel N. Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex. eLife 2023; 12:77690. [PMID: 37166452 PMCID: PMC10174693 DOI: 10.7554/elife.77690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/09/2023] [Indexed: 05/12/2023] Open
Abstract
The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.
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Affiliation(s)
| | - Nicholas G Hatsopoulos
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, United States
- Committee on Computational Neuroscience, University of Chicago, Chicago, United States
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, United States
- Department of Physics, Duke University, Durham, United States
- Duke Institute for Brain Sciences, Duke University, Durham, United States
- Center for Cognitive Neuroscience, Duke University, Durham, United States
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14
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Global inhibition in head-direction neural circuits: a systematic comparison between connectome-based spiking neural circuit models. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2023:10.1007/s00359-023-01615-z. [PMID: 36781446 DOI: 10.1007/s00359-023-01615-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/15/2023]
Abstract
The recent discovery of the head-direction (HD) system in fruit flies has provided unprecedented insights into the neural mechanisms of spatial orientation. Despite the progress, the neural substance of global inhibition, an essential component of the HD circuits, remains controversial. Some studies suggested that the ring neurons provide global inhibition, while others suggested the Δ7 neurons. In the present study, we provide evaluations from the theoretical perspective by performing systematic analyses on the computational models based on the ring-neuron (R models) and Δ7-neurons (Delta models) hypotheses with modifications according to the latest connectomic data. We conducted four tests: robustness, persistency, speed, and dynamical characteristics. We discovered that the two models led to a comparable performance in general, but each excelled in different tests. The R Models were more robust, while the Delta models were better in the persistency test. We also tested a hybrid model that combines both inhibitory mechanisms. While the performances of the R and Delta models in each test are highly parameter-dependent, the Hybrid model performed well in all tests with the same set of parameters. Our results suggest the possibility of combined inhibitory mechanisms in the HD circuits of fruit flies.
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15
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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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16
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Kozachkov L, Tauber J, Lundqvist M, Brincat SL, Slotine JJ, Miller EK. Robust and brain-like working memory through short-term synaptic plasticity. PLoS Comput Biol 2022; 18:e1010776. [PMID: 36574424 PMCID: PMC9829165 DOI: 10.1371/journal.pcbi.1010776] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 01/09/2023] [Accepted: 11/29/2022] [Indexed: 12/29/2022] Open
Abstract
Working memory has long been thought to arise from sustained spiking/attractor dynamics. However, recent work has suggested that short-term synaptic plasticity (STSP) may help maintain attractor states over gaps in time with little or no spiking. To determine if STSP endows additional functional advantages, we trained artificial recurrent neural networks (RNNs) with and without STSP to perform an object working memory task. We found that RNNs with and without STSP were able to maintain memories despite distractors presented in the middle of the memory delay. However, RNNs with STSP showed activity that was similar to that seen in the cortex of a non-human primate (NHP) performing the same task. By contrast, RNNs without STSP showed activity that was less brain-like. Further, RNNs with STSP were more robust to network degradation than RNNs without STSP. These results show that STSP can not only help maintain working memories, it also makes neural networks more robust and brain-like.
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Affiliation(s)
- Leo Kozachkov
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Nonlinear Systems Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
| | - John Tauber
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
| | - Mikael Lundqvist
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Scott L. Brincat
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
| | - Jean-Jacques Slotine
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Nonlinear Systems Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
| | - Earl K. Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- * E-mail:
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17
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Cihak HL, Eissa TL, Kilpatrick ZP. Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field. SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS 2022; 21:2579-2609. [PMID: 38250343 PMCID: PMC10798676 DOI: 10.1137/22m1482329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity "bumps" represent the continuous location of a cue over several seconds. Pyramidal (excitatory (E )) and interneuronal (inhibitory (I )) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint E /I (lateral inhibitory) population and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate E /I populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E /I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance.
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Affiliation(s)
- Heather L Cihak
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80309 USA
| | - Tahra L Eissa
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80309 USA
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80309 USA
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18
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Wang R, Kang L. Multiple bumps can enhance robustness to noise in continuous attractor networks. PLoS Comput Biol 2022; 18:e1010547. [PMID: 36215305 PMCID: PMC9584540 DOI: 10.1371/journal.pcbi.1010547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 10/20/2022] [Accepted: 09/06/2022] [Indexed: 11/19/2022] Open
Abstract
A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells, which maintain periodic representations of position and orientation, respectively. These representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We address this knowledge gap by constructing 1D ring attractor networks with different bump numbers and characterizing their responses to three types of noise: fluctuating inputs, spiking noise, and deviations in connectivity away from ideal attractor configurations. Across all three types, networks with more bumps experience less noise-driven deviations in bump motion. This translates to more robust encodings of linear coordinates, like position, assuming that each neuron represents a fixed length no matter the bump number. Alternatively, we consider encoding a circular coordinate, like orientation, such that the network distance between adjacent bumps always maps onto 360 degrees. Under this mapping, bump number does not significantly affect the amount of error in the coordinate readout. Our simulation results are intuitively explained and quantitatively matched by a unified theory for path integration and noise in multi-bump networks. Thus, to suppress the effects of biologically relevant noise, continuous attractor networks can employ more bumps when encoding linear coordinates; this advantage disappears when encoding circular coordinates. Our findings provide motivation for multiple bumps in the mammalian grid network.
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Affiliation(s)
- Raymond Wang
- Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, United States of America
- Neural Circuits and Computations Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Louis Kang
- Neural Circuits and Computations Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
- * E-mail:
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19
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Becker S, Nold A, Tchumatchenko T. Modulation of working memory duration by synaptic and astrocytic mechanisms. PLoS Comput Biol 2022; 18:e1010543. [PMID: 36191056 PMCID: PMC9560596 DOI: 10.1371/journal.pcbi.1010543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 10/13/2022] [Accepted: 09/05/2022] [Indexed: 12/24/2022] Open
Abstract
Short-term synaptic plasticity and modulations of the presynaptic vesicle release rate are key components of many working memory models. At the same time, an increasing number of studies suggests a potential role of astrocytes in modulating higher cognitive function such as WM through their influence on synaptic transmission. Which influence astrocytic signaling could have on the stability and duration of WM representations, however, is still unclear. Here, we introduce a slow, activity-dependent astrocytic regulation of the presynaptic release probability in a synaptic attractor model of WM. We compare and analyze simulations of a simple WM protocol in firing rate and spiking networks with and without astrocytic regulation, and underpin our observations with analyses of the phase space dynamics in the rate network. We find that the duration and stability of working memory representations are altered by astrocytic signaling and by noise. We show that astrocytic signaling modulates the mean duration of WM representations. Moreover, if the astrocytic regulation is strong, a slow presynaptic timescale introduces a 'window of vulnerability', during which WM representations are easily disruptable by noise before being stabilized. We identify two mechanisms through which noise from different sources in the network can either stabilize or destabilize WM representations. Our findings suggest that (i) astrocytic regulation can act as a crucial determinant for the duration of WM representations in synaptic attractor models of WM, and (ii) that astrocytic signaling could facilitate different mechanisms for volitional top-down control of WM representations and their duration.
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Affiliation(s)
- Sophia Becker
- Laboratory of Computational Neuroscience, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | - Andreas Nold
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Institute of Experimental Epileptology and Cognition Research, Life and Brain Center, Universitätsklinikum Bonn, Bonn, Germany
| | - Tatjana Tchumatchenko
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Institute of Experimental Epileptology and Cognition Research, Life and Brain Center, Universitätsklinikum Bonn, Bonn, Germany
- Institute for Physiological Chemistry, Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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20
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Gu J, Lim S. Unsupervised learning for robust working memory. PLoS Comput Biol 2022; 18:e1009083. [PMID: 35500033 PMCID: PMC9098088 DOI: 10.1371/journal.pcbi.1009083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/12/2022] [Accepted: 03/16/2022] [Indexed: 11/18/2022] Open
Abstract
Working memory is a core component of critical cognitive functions such as planning and decision-making. Persistent activity that lasts long after the stimulus offset has been considered a neural substrate for working memory. Attractor dynamics based on network interactions can successfully reproduce such persistent activity. However, it requires a fine-tuning of network connectivity, in particular, to form continuous attractors which were suggested for encoding continuous signals in working memory. Here, we investigate whether a specific form of synaptic plasticity rules can mitigate such tuning problems in two representative working memory models, namely, rate-coded and location-coded persistent activity. We consider two prominent types of plasticity rules, differential plasticity correcting the rapid activity changes and homeostatic plasticity regularizing the long-term average of activity, both of which have been proposed to fine-tune the weights in an unsupervised manner. Consistent with the findings of previous works, differential plasticity alone was enough to recover a graded-level persistent activity after perturbations in the connectivity. For the location-coded memory, differential plasticity could also recover persistent activity. However, its pattern can be irregular for different stimulus locations under slow learning speed or large perturbation in the connectivity. On the other hand, homeostatic plasticity shows a robust recovery of smooth spatial patterns under particular types of synaptic perturbations, such as perturbations in incoming synapses onto the entire or local populations. However, homeostatic plasticity was not effective against perturbations in outgoing synapses from local populations. Instead, combining it with differential plasticity recovers location-coded persistent activity for a broader range of perturbations, suggesting compensation between two plasticity rules.
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Affiliation(s)
- Jintao Gu
- Neural Science, New York University Shanghai, Shanghai, China
| | - Sukbin Lim
- Neural Science, New York University Shanghai, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China
- * E-mail:
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21
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Mejías JF, Wang XJ. Mechanisms of distributed working memory in a large-scale network of macaque neocortex. eLife 2022; 11:e72136. [PMID: 35200137 PMCID: PMC8871396 DOI: 10.7554/elife.72136] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 01/19/2022] [Indexed: 12/15/2022] Open
Abstract
Neural activity underlying working memory is not a local phenomenon but distributed across multiple brain regions. To elucidate the circuit mechanism of such distributed activity, we developed an anatomically constrained computational model of large-scale macaque cortex. We found that mnemonic internal states may emerge from inter-areal reverberation, even in a regime where none of the isolated areas is capable of generating self-sustained activity. The mnemonic activity pattern along the cortical hierarchy indicates a transition in space, separating areas engaged in working memory and those which do not. A host of spatially distinct attractor states is found, potentially subserving various internal processes. The model yields testable predictions, including the idea of counterstream inhibitory bias, the role of prefrontal areas in controlling distributed attractors, and the resilience of distributed activity to lesions or inactivation. This work provides a theoretical framework for identifying large-scale brain mechanisms and computational principles of distributed cognitive processes.
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Affiliation(s)
- Jorge F Mejías
- Swammerdam Institute for Life Sciences, University of AmsterdamAmsterdamNetherlands
| | - Xiao-Jing Wang
- Center for Neural Science, New York UniversityNew YorkUnited States
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22
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Stein H, Barbosa J, Compte A. Towards biologically constrained attractor models of schizophrenia. Curr Opin Neurobiol 2021; 70:171-181. [PMID: 34839146 DOI: 10.1016/j.conb.2021.10.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 10/19/2021] [Accepted: 10/27/2021] [Indexed: 12/31/2022]
Abstract
Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing alterations in memory circuits can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing biophysical accounts. Critical points to resolve are the effects of increases vs. decreases in E/I ratio (e.g. through NMDAR blockade) on firing rate tuning and shared noise modulations and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.
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Affiliation(s)
- Heike Stein
- Laboratoire de Neurosciences Cognitives et Computationnelles, Département d'Études Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France
| | - Joao Barbosa
- Laboratoire de Neurosciences Cognitives et Computationnelles, Département d'Études Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France
| | - Albert Compte
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
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23
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Do Q, Hasselmo ME. Neural circuits and symbolic processing. Neurobiol Learn Mem 2021; 186:107552. [PMID: 34763073 PMCID: PMC10121157 DOI: 10.1016/j.nlm.2021.107552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 10/14/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022]
Abstract
The ability to use symbols is a defining feature of human intelligence. However, neuroscience has yet to explain the fundamental neural circuit mechanisms for flexibly representing and manipulating abstract concepts. This article will review the research on neural models for symbolic processing. The review first focuses on the question of how symbols could possibly be represented in neural circuits. The review then addresses how neural symbolic representations could be flexibly combined to meet a wide range of reasoning demands. Finally, the review assesses the research on program synthesis and proposes that the most flexible neural representation of symbolic processing would involve the capacity to rapidly synthesize neural operations analogous to lambda calculus to solve complex cognitive tasks.
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Affiliation(s)
- Quan Do
- Center for Systems Neuroscience, Boston University, 610 Commonwealth Ave, Boston, MA 02215, United States.
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Boston University, 610 Commonwealth Ave, Boston, MA 02215, United States.
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24
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Barbosa J, Lozano-Soldevilla D, Compte A. Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories. PLoS Biol 2021; 19:e3001436. [PMID: 34673775 PMCID: PMC8641864 DOI: 10.1371/journal.pbio.3001436] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 12/03/2021] [Accepted: 10/04/2021] [Indexed: 11/18/2022] Open
Abstract
Persistently active neurons during mnemonic periods have been regarded as the mechanism underlying working memory maintenance. Alternatively, neuronal networks could instead store memories in fast synaptic changes, thus avoiding the biological cost of maintaining an active code through persistent neuronal firing. Such "activity-silent" codes have been proposed for specific conditions in which memories are maintained in a nonprioritized state, as for unattended but still relevant short-term memories. A hallmark of this "activity-silent" code is that these memories can be reactivated from silent, synaptic traces. Evidence for "activity-silent" working memory storage has come from human electroencephalography (EEG), in particular from the emergence of decodability (EEG reactivations) induced by visual impulses (termed pinging) during otherwise "silent" periods. Here, we reanalyze EEG data from such pinging studies. We find that the originally reported absence of memory decoding reflects weak statistical power, as decoding is possible based on more powered analyses or reanalysis using alpha power instead of raw voltage. This reveals that visual pinging EEG "reactivations" occur in the presence of an electrically active, not silent, code for unattended memories in these data. This crucial change in the evidence provided by this dataset prompts a reinterpretation of the mechanisms of EEG reactivations. We provide 2 possible explanations backed by computational models, and we discuss the relationship with TMS-induced EEG reactivations.
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Affiliation(s)
- Joao Barbosa
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Laboratoire de Neurosciences Cognitives et Computationnelles, Département d’Études Cognitives, École Normale Supérieure, PSL University, Paris, France
| | - Diego Lozano-Soldevilla
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, Pozuelo de Alarcón, Madrid, Spain
| | - Albert Compte
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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25
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Slow manifolds within network dynamics encode working memory efficiently and robustly. PLoS Comput Biol 2021; 17:e1009366. [PMID: 34525089 PMCID: PMC8475983 DOI: 10.1371/journal.pcbi.1009366] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 09/27/2021] [Accepted: 08/19/2021] [Indexed: 11/19/2022] Open
Abstract
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.
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26
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Pulvermüller F, Tomasello R, Henningsen-Schomers MR, Wennekers T. Biological constraints on neural network models of cognitive function. Nat Rev Neurosci 2021; 22:488-502. [PMID: 34183826 PMCID: PMC7612527 DOI: 10.1038/s41583-021-00473-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2021] [Indexed: 02/06/2023]
Abstract
Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling.
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Affiliation(s)
- Friedemann Pulvermüller
- Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, Berlin, Germany.
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
- Einstein Center for Neurosciences Berlin, Berlin, Germany.
- Cluster of Excellence 'Matters of Activity', Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Rosario Tomasello
- Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, Berlin, Germany
- Cluster of Excellence 'Matters of Activity', Humboldt-Universität zu Berlin, Berlin, Germany
| | - Malte R Henningsen-Schomers
- Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, Berlin, Germany
- Cluster of Excellence 'Matters of Activity', Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thomas Wennekers
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
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27
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Flores-Valle A, Gonçalves PJ, Seelig JD. Integration of sleep homeostasis and navigation in Drosophila. PLoS Comput Biol 2021; 17:e1009088. [PMID: 34252086 PMCID: PMC8297946 DOI: 10.1371/journal.pcbi.1009088] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 07/22/2021] [Accepted: 05/17/2021] [Indexed: 11/25/2022] Open
Abstract
During sleep, the brain undergoes dynamic and structural changes. In Drosophila, such changes have been observed in the central complex, a brain area important for sleep control and navigation. The connectivity of the central complex raises the question about how navigation, and specifically the head direction system, can operate in the face of sleep related plasticity. To address this question, we develop a model that integrates sleep homeostasis and head direction. We show that by introducing plasticity, the head direction system can function in a stable way by balancing plasticity in connected circuits that encode sleep pressure. With increasing sleep pressure, the head direction system nevertheless becomes unstable and a sleep phase with a different plasticity mechanism is introduced to reset network connectivity. The proposed integration of sleep homeostasis and head direction circuits captures features of their neural dynamics observed in flies and mice.
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Affiliation(s)
- Andres Flores-Valle
- Center of Advanced European Studies and Research (caesar), Bonn, Germany
- International Max Planck Research School for Brain and Behavior, Bonn, Germany
| | - Pedro J. Gonçalves
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany
| | - Johannes D. Seelig
- Center of Advanced European Studies and Research (caesar), Bonn, Germany
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28
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Berberian N, Ross M, Chartier S. Embodied working memory during ongoing input streams. PLoS One 2021; 16:e0244822. [PMID: 33400724 PMCID: PMC7785253 DOI: 10.1371/journal.pone.0244822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 12/16/2020] [Indexed: 11/18/2022] Open
Abstract
Sensory stimuli endow animals with the ability to generate an internal representation. This representation can be maintained for a certain duration in the absence of previously elicited inputs. The reliance on an internal representation rather than purely on the basis of external stimuli is a hallmark feature of higher-order functions such as working memory. Patterns of neural activity produced in response to sensory inputs can continue long after the disappearance of previous inputs. Experimental and theoretical studies have largely invested in understanding how animals faithfully maintain sensory representations during ongoing reverberations of neural activity. However, these studies have focused on preassigned protocols of stimulus presentation, leaving out by default the possibility of exploring how the content of working memory interacts with ongoing input streams. Here, we study working memory using a network of spiking neurons with dynamic synapses subject to short-term and long-term synaptic plasticity. The formal model is embodied in a physical robot as a companion approach under which neuronal activity is directly linked to motor output. The artificial agent is used as a methodological tool for studying the formation of working memory capacity. To this end, we devise a keyboard listening framework to delineate the context under which working memory content is (1) refined, (2) overwritten or (3) resisted by ongoing new input streams. Ultimately, this study takes a neurorobotic perspective to resurface the long-standing implication of working memory in flexible cognition.
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Affiliation(s)
- Nareg Berberian
- Laboratory for Computational Neurodynamics and Cognition, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Matt Ross
- Laboratory for Computational Neurodynamics and Cognition, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Sylvain Chartier
- Laboratory for Computational Neurodynamics and Cognition, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
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29
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Turner-Evans DB, Jensen KT, Ali S, Paterson T, Sheridan A, Ray RP, Wolff T, Lauritzen JS, Rubin GM, Bock DD, Jayaraman V. The Neuroanatomical Ultrastructure and Function of a Biological Ring Attractor. Neuron 2020; 108:145-163.e10. [PMID: 32916090 PMCID: PMC8356802 DOI: 10.1016/j.neuron.2020.08.006] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 05/20/2020] [Accepted: 08/05/2020] [Indexed: 01/31/2023]
Abstract
Neural representations of head direction (HD) have been discovered in many species. Theoretical work has proposed that the dynamics associated with these representations are generated, maintained, and updated by recurrent network structures called ring attractors. We evaluated this theorized structure-function relationship by performing electron-microscopy-based circuit reconstruction and RNA profiling of identified cell types in the HD system of Drosophila melanogaster. We identified motifs that have been hypothesized to maintain the HD representation in darkness, update it when the animal turns, and tether it to visual cues. Functional studies provided support for the proposed roles of individual excitatory or inhibitory circuit elements in shaping activity. We also discovered recurrent connections between neuronal arbors with mixed pre- and postsynaptic specializations. Our results confirm that the Drosophila HD network contains the core components of a ring attractor while also revealing unpredicted structural features that might enhance the network's computational power.
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Affiliation(s)
| | - Kristopher T Jensen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Saba Ali
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tyler Paterson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Arlo Sheridan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Robert P Ray
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tanya Wolff
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - J Scott Lauritzen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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30
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Reduced serial dependence suggests deficits in synaptic potentiation in anti-NMDAR encephalitis and schizophrenia. Nat Commun 2020; 11:4250. [PMID: 32843635 PMCID: PMC7447775 DOI: 10.1038/s41467-020-18033-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 07/31/2020] [Indexed: 01/06/2023] Open
Abstract
A mechanistic understanding of core cognitive processes, such as working memory, is crucial to addressing psychiatric symptoms in brain disorders. We propose a combined psychophysical and biophysical account of two symptomatologically related diseases, both linked to hypofunctional NMDARs: schizophrenia and autoimmune anti-NMDAR encephalitis. We first quantified shared working memory alterations in a delayed-response task. In both patient groups, we report a markedly reduced influence of previous stimuli on working memory contents, despite preserved memory precision. We then simulated this finding with NMDAR-dependent synaptic alterations in a microcircuit model of prefrontal cortex. Changes in cortical excitation destabilized within-trial memory maintenance and could not account for disrupted serial dependence in working memory. Rather, a quantitative fit between data and simulations supports alterations of an NMDAR-dependent memory mechanism operating on longer timescales, such as short-term potentiation. Stein, Barbosa et al. show that anti-NMDAR encephalitis and schizophrenia are characterized by reduced serial dependence in spatial working memory. Cortical network simulations show that this can be parsimoniously explained by a reduction in NMDAR-dependent short-term synaptic potentiation in these diseases.
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31
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Barbosa J, Stein H, Martinez RL, Galan-Gadea A, Li S, Dalmau J, Adam KCS, Valls-Solé J, Constantinidis C, Compte A. Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory. Nat Neurosci 2020; 23:1016-1024. [PMID: 32572236 PMCID: PMC7392810 DOI: 10.1038/s41593-020-0644-4] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 04/21/2020] [Indexed: 11/16/2022]
Abstract
Persistent neuronal spiking has long been considered the mechanism underlying working memory, but recent proposals argue for alternative 'activity-silent' substrates. Using monkey and human electrophysiology data, we show here that attractor dynamics that control neural spiking during mnemonic periods interact with activity-silent mechanisms in the prefrontal cortex (PFC). This interaction allows memory reactivations, which enhance serial biases in spatial working memory. Stimulus information was not decodable between trials, but remained present in activity-silent traces inferred from spiking synchrony in the PFC. Just before the new stimulus, this latent trace was reignited into activity that recapitulated the previous stimulus representation. Importantly, the reactivation strength correlated with the strength of serial biases in both monkeys and humans, as predicted by a computational model that integrates activity-based and activity-silent mechanisms. Finally, single-pulse transcranial magnetic stimulation applied to the human PFC between successive trials enhanced serial biases, thus demonstrating the causal role of prefrontal reactivations in determining working-memory behavior.
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Affiliation(s)
- Joao Barbosa
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Heike Stein
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Rebecca L Martinez
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Adrià Galan-Gadea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Sihai Li
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Josep Dalmau
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Service of Neurology, Hospital Clínic, Barcelona, Spain
- University of Barcelona, Barcelona, Spain
- ICREA, Barcelona, Spain
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Kirsten C S Adam
- Department of Psychology and Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Josep Valls-Solé
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Christos Constantinidis
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Albert Compte
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
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32
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Huyck CR, Vergani AA. Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons. J Comput Neurosci 2020; 48:299-316. [PMID: 32715350 DOI: 10.1007/s10827-020-00758-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 11/27/2022]
Abstract
Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; however, these bumps can be merged by overcoming the repeller dynamics. A simple associative memory can include these bump attractors, allowing the use of continuous variables in these memories, and these associations can be learned by Hebbian rules. These simulations are related to biological networks, showing that this is a step toward a more complete neural cognitive associative memory.
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33
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Hey, look over there: Distraction effects on rapid sequence recall. PLoS One 2020; 15:e0223743. [PMID: 32275703 PMCID: PMC7147745 DOI: 10.1371/journal.pone.0223743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 03/11/2020] [Indexed: 11/19/2022] Open
Abstract
In the course of everyday life, the brain must store and recall a huge variety of representations of stimuli which are presented in an ordered or sequential way. The processes by which the ordering of these various things is stored and recalled are moderately well understood. We use here a computational model of a cortex-like recurrent neural network adapted by a multitude of plasticity mechanisms. We first demonstrate the learning of a sequence. Then, we examine the influence of different types of distractors on the network dynamics during the recall of the encoded ordered information being ordered in a sequence. We are able to broadly arrive at two distinct effect-categories for distractors, arrive at a basic understanding of why this is so, and predict what distractors will fall into each category.
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34
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Schmutz V, Gerstner W, Schwalger T. Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2020; 10:5. [PMID: 32253526 PMCID: PMC7136387 DOI: 10.1186/s13408-020-00082-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 03/25/2020] [Indexed: 06/07/2023]
Abstract
Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static synapses to the case of dynamic synapses exhibiting short-term plasticity (STP). The extended theory offers an approximate mean-field dynamics for the synaptic input currents arising from populations of spiking neurons and synapses undergoing Tsodyks-Markram STP. The approximate mean-field dynamics accounts for both finite number of synapses and correlation between the two synaptic variables of the model (utilization and available resources) and its numerical implementation is simple. Comparisons with Monte Carlo simulations of the microscopic model show that in both feedforward and recurrent networks, the mesoscopic mean-field model accurately reproduces the first- and second-order statistics of the total synaptic input into a postsynaptic neuron and accounts for stochastic switches between Up and Down states and for population spikes. The extended mesoscopic population theory of spiking neural networks with STP may be useful for a systematic reduction of detailed biophysical models of cortical microcircuits to numerically efficient and mathematically tractable mean-field models.
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Affiliation(s)
- Valentin Schmutz
- Brain Mind Institute, École Polytechnique Féderale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Wulfram Gerstner
- Brain Mind Institute, École Polytechnique Féderale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tilo Schwalger
- Brain Mind Institute, École Polytechnique Féderale de Lausanne (EPFL), Lausanne, Switzerland
- Bernstein Center for Computational Neuroscience, Institut für Mathematik, Technische Universität Berlin, Berlin, Germany
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35
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Brock JA, Thomazeau A, Watanabe A, Li SSY, Sjöström PJ. A Practical Guide to Using CV Analysis for Determining the Locus of Synaptic Plasticity. Front Synaptic Neurosci 2020; 12:11. [PMID: 32292337 PMCID: PMC7118219 DOI: 10.3389/fnsyn.2020.00011] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 03/04/2020] [Indexed: 01/17/2023] Open
Abstract
Long-term synaptic plasticity is widely believed to underlie learning and memory in the brain. Whether plasticity is primarily expressed pre- or postsynaptically has been the subject of considerable debate for many decades. More recently, it is generally agreed that the locus of plasticity depends on a number of factors, such as developmental stage, induction protocol, and synapse type. Since presynaptic expression alters not just the gain but also the short-term dynamics of a synapse, whereas postsynaptic expression only modifies the gain, the locus has fundamental implications for circuits dynamics and computations in the brain. It therefore remains crucial for our understanding of neuronal circuits to know the locus of expression of long-term plasticity. One classical method for elucidating whether plasticity is pre- or postsynaptically expressed is based on analysis of the coefficient of variation (CV), which serves as a measure of noise levels of synaptic neurotransmission. Here, we provide a practical guide to using CV analysis for the purposes of exploring the locus of expression of long-term plasticity, primarily aimed at beginners in the field. We provide relatively simple intuitive background to an otherwise theoretically complex approach as well as simple mathematical derivations for key parametric relationships. We list important pitfalls of the method, accompanied by accessible computer simulations to better illustrate the problems (downloadable from GitHub), and we provide straightforward solutions for these issues.
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Affiliation(s)
- Jennifer A Brock
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Medicine, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Aurore Thomazeau
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Medicine, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
| | - Airi Watanabe
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Medicine, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Sally Si Ying Li
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - P Jesper Sjöström
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Medicine, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
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