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Zhang Z, Tang F, Li Y, Feng X. A spatial transformation-based CAN model for information integration within grid cell modules. Cogn Neurodyn 2024; 18:1861-1876. [PMID: 39104694 PMCID: PMC11297887 DOI: 10.1007/s11571-023-10047-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/13/2023] [Accepted: 11/26/2023] [Indexed: 08/07/2024] Open
Abstract
The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.
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Affiliation(s)
- Zhihui Zhang
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Fengzhen Tang
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Yiping Li
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Xisheng Feng
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
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2
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Dong LL, Fiete IR. Grid Cells in Cognition: Mechanisms and Function. Annu Rev Neurosci 2024; 47:345-368. [PMID: 38684081 DOI: 10.1146/annurev-neuro-101323-112047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.
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Affiliation(s)
- Ling L Dong
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Ila R Fiete
- McGovern Institute and K. Lisa Yang Integrative Computational Neuroscience Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
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3
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Rolls ET, Treves A. A theory of hippocampal function: New developments. Prog Neurobiol 2024; 238:102636. [PMID: 38834132 DOI: 10.1016/j.pneurobio.2024.102636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/15/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
We develop further here the only quantitative theory of the storage of information in the hippocampal episodic memory system and its recall back to the neocortex. The theory is upgraded to account for a revolution in understanding of spatial representations in the primate, including human, hippocampus, that go beyond the place where the individual is located, to the location being viewed in a scene. This is fundamental to much primate episodic memory and navigation: functions supported in humans by pathways that build 'where' spatial view representations by feature combinations in a ventromedial visual cortical stream, separate from those for 'what' object and face information to the inferior temporal visual cortex, and for reward information from the orbitofrontal cortex. Key new computational developments include the capacity of the CA3 attractor network for storing whole charts of space; how the correlations inherent in self-organizing continuous spatial representations impact the storage capacity; how the CA3 network can combine continuous spatial and discrete object and reward representations; the roles of the rewards that reach the hippocampus in the later consolidation into long-term memory in part via cholinergic pathways from the orbitofrontal cortex; and new ways of analysing neocortical information storage using Potts networks.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
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4
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Zhang Z, Tang F, Li Y, Feng X. Modeling the grid cell activity based on cognitive space transformation. Cogn Neurodyn 2024; 18:1227-1243. [PMID: 38826659 PMCID: PMC11143158 DOI: 10.1007/s11571-023-09972-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/09/2023] [Accepted: 04/11/2023] [Indexed: 06/04/2024] Open
Abstract
The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.
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Affiliation(s)
- Zhihui Zhang
- University of Science and Technology of China, Hefei, 230026 China
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
| | - Fengzhen Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
| | - Yiping Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
| | - Xisheng Feng
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
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5
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Choudhary K, Berberich S, Hahn TTG, McFarland JM, Mehta MR. Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity. Nat Commun 2024; 15:3542. [PMID: 38719802 PMCID: PMC11079062 DOI: 10.1038/s41467-024-47617-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 04/04/2024] [Indexed: 05/12/2024] Open
Abstract
Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.
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Affiliation(s)
- Krishna Choudhary
- Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA, USA
- HRL Laboratories, Malibu, CA, USA
| | - Sven Berberich
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | | | | | - Mayank R Mehta
- Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA, USA.
- W. M. Keck Center for Neurophysics, University of California, Los Angeles, CA, USA.
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
- Departments of Neurology and Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA.
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6
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Malone TJ, Tien NW, Ma Y, Cui L, Lyu S, Wang G, Nguyen D, Zhang K, Myroshnychenko MV, Tyan J, Gordon JA, Kupferschmidt DA, Gu Y. A consistent map in the medial entorhinal cortex supports spatial memory. Nat Commun 2024; 15:1457. [PMID: 38368457 PMCID: PMC10874432 DOI: 10.1038/s41467-024-45853-4] [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: 09/20/2023] [Accepted: 02/05/2024] [Indexed: 02/19/2024] Open
Abstract
The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.
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Affiliation(s)
- Taylor J Malone
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nai-Wen Tien
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Yan Ma
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lian Cui
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Shangru Lyu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Garret Wang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Duc Nguyen
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- Center of Neural Science, New York University, New York, NY, USA
| | - Kai Zhang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Maxym V Myroshnychenko
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jean Tyan
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Joshua A Gordon
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Office of the Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David A Kupferschmidt
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Yi Gu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
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7
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Reinshagen A. Grid cells: the missing link in understanding Parkinson's disease? Front Neurosci 2024; 18:1276714. [PMID: 38389787 PMCID: PMC10881698 DOI: 10.3389/fnins.2024.1276714] [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: 08/12/2023] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
The mechanisms underlying Parkinson's disease (PD) are complex and not fully understood, and the box-and-arrow model among other current models present significant challenges. This paper explores the potential role of the allocentric brain and especially its grid cells in several PD motor symptoms, including bradykinesia, kinesia paradoxa, freezing of gait, the bottleneck phenomenon, and their dependency on cueing. It is argued that central hubs, like the locus coeruleus and the pedunculopontine nucleus, often narrowly interpreted in the context of PD, play an equally important role in governing the allocentric brain as the basal ganglia. Consequently, the motor and secondary motor (e.g., spatially related) symptoms of PD linked with dopamine depletion may be more closely tied to erroneous computation by grid cells than to the basal ganglia alone. Because grid cells and their associated central hubs introduce both spatial and temporal information to the brain influencing velocity perception they may cause bradykinesia or hyperkinesia as well. In summary, PD motor symptoms may primarily be an allocentric disturbance resulting from virtual faulty computation by grid cells revealed by dopamine depletion in PD.
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8
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Malone TJ, Tien NW, Ma Y, Cui L, Lyu S, Wang G, Nguyen D, Zhang K, Myroshnychenko MV, Tyan J, Gordon JA, Kupferschmidt DA, Gu Y. A consistent map in the medial entorhinal cortex supports spatial memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560254. [PMID: 37986767 PMCID: PMC10659391 DOI: 10.1101/2023.09.30.560254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.
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Affiliation(s)
- Taylor J. Malone
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- These authors contributed equally to this work
| | - Nai-Wen Tien
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Current address: Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- These authors contributed equally to this work
| | - Yan Ma
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- These authors contributed equally to this work
| | - Lian Cui
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shangru Lyu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Garret Wang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Duc Nguyen
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Current address: Center of Neural Science, New York University, New York, NY, USA
| | - Kai Zhang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Maxym V. Myroshnychenko
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jean Tyan
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joshua A. Gordon
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
- Office of the Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - David A. Kupferschmidt
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yi Gu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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9
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Shi Y, Cui H, Li X, Chen L, Zhang C, Zhao X, Li X, Shao Q, Sun Q, Yan K, Wang G. Laminar and dorsoventral organization of layer 1 interneuronal microcircuitry in superficial layers of the medial entorhinal cortex. Cell Rep 2023; 42:112782. [PMID: 37436894 DOI: 10.1016/j.celrep.2023.112782] [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: 11/01/2022] [Revised: 04/03/2023] [Accepted: 06/24/2023] [Indexed: 07/14/2023] Open
Abstract
Layer 1 (L1) interneurons (INs) participate in various brain functions by gating information flow in the neocortex, but their role in the medial entorhinal cortex (MEC) is still unknown, largely due to scant knowledge of MEC L1 microcircuitry. Using simultaneous triple-octuple whole-cell recordings and morphological reconstructions, we comprehensively depict L1IN networks in the MEC. We identify three morphologically distinct types of L1INs with characteristic electrophysiological properties. We dissect intra- and inter-laminar cell-type-specific microcircuits of L1INs, showing connectivity patterns different from those in the neocortex. Remarkably, motif analysis reveals transitive and clustered features of L1 networks, as well as over-represented trans-laminar motifs. Finally, we demonstrate the dorsoventral gradient of L1IN microcircuits, with dorsal L1 neurogliaform cells receiving fewer intra-laminar inputs but exerting more inhibition on L2 principal neurons. These results thus present a more comprehensive picture of L1IN microcircuitry, which is indispensable for deciphering the function of L1INs in the MEC.
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Affiliation(s)
- Yuying Shi
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Hui Cui
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Xiaoyue Li
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ligu Chen
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Chen Zhang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Xinran Zhao
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Xiaowan Li
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Qiming Shao
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Qiang Sun
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Kaiyue Yan
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Guangfu Wang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
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10
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Ginosar G, Aljadeff J, Las L, Derdikman D, Ulanovsky N. Are grid cells used for navigation? On local metrics, subjective spaces, and black holes. Neuron 2023; 111:1858-1875. [PMID: 37044087 DOI: 10.1016/j.neuron.2023.03.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/18/2022] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
The symmetric, lattice-like spatial pattern of grid-cell activity is thought to provide a neuronal global metric for space. This view is compatible with grid cells recorded in empty boxes but inconsistent with data from more naturalistic settings. We review evidence arguing against the global-metric notion, including the distortion and disintegration of the grid pattern in complex and three-dimensional environments. We argue that deviations from lattice symmetry are key for understanding grid-cell function. We propose three possible functions for grid cells, which treat real-world grid distortions as a feature rather than a bug. First, grid cells may constitute a local metric for proximal space rather than a global metric for all space. Second, grid cells could form a metric for subjective action-relevant space rather than physical space. Third, distortions may represent salient locations. Finally, we discuss mechanisms that can underlie these functions. These ideas may transform our thinking about grid cells.
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Affiliation(s)
- Gily Ginosar
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Johnatan Aljadeff
- Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Liora Las
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Dori Derdikman
- Department of Neuroscience, Rappaport Faculty of Medicine and Research Institute, Technion, Haifa 31096, Israel.
| | - Nachum Ulanovsky
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
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11
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Langdon C, Genkin M, Engel TA. A unifying perspective on neural manifolds and circuits for cognition. Nat Rev Neurosci 2023; 24:363-377. [PMID: 37055616 PMCID: PMC11058347 DOI: 10.1038/s41583-023-00693-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 04/15/2023]
Abstract
Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.
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Affiliation(s)
- Christopher Langdon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Mikhail Genkin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Tatiana A Engel
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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12
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Alexander AS, Robinson JC, Stern CE, Hasselmo ME. Gated transformations from egocentric to allocentric reference frames involving retrosplenial cortex, entorhinal cortex, and hippocampus. Hippocampus 2023; 33:465-487. [PMID: 36861201 PMCID: PMC10403145 DOI: 10.1002/hipo.23513] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 03/03/2023]
Abstract
This paper reviews the recent experimental finding that neurons in behaving rodents show egocentric coding of the environment in a number of structures associated with the hippocampus. Many animals generating behavior on the basis of sensory input must deal with the transformation of coordinates from the egocentric position of sensory input relative to the animal, into an allocentric framework concerning the position of multiple goals and objects relative to each other in the environment. Neurons in retrosplenial cortex show egocentric coding of the position of boundaries in relation to an animal. These neuronal responses are discussed in relation to existing models of the transformation from egocentric to allocentric coordinates using gain fields and a new model proposing transformations of phase coding that differ from current models. The same type of transformations could allow hierarchical representations of complex scenes. The responses in rodents are also discussed in comparison to work on coordinate transformations in humans and non-human primates.
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Affiliation(s)
- Andrew S Alexander
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Jennifer C Robinson
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Chantal E Stern
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
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13
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Rolls ET. Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans. Hippocampus 2023; 33:533-572. [PMID: 36070199 PMCID: PMC10946493 DOI: 10.1002/hipo.23467] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 01/08/2023]
Abstract
Hippocampal and parahippocampal gyrus spatial view neurons in primates respond to the spatial location being looked at. The representation is allocentric, in that the responses are to locations "out there" in the world, and are relatively invariant with respect to retinal position, eye position, head direction, and the place where the individual is located. The underlying connectivity in humans is from ventromedial visual cortical regions to the parahippocampal scene area, leading to the theory that spatial view cells are formed by combinations of overlapping feature inputs self-organized based on their closeness in space. Thus, although spatial view cells represent "where" for episodic memory and navigation, they are formed by ventral visual stream feature inputs in the parahippocampal gyrus in what is the parahippocampal scene area. A second "where" driver of spatial view cells are parietal inputs, which it is proposed provide the idiothetic update for spatial view cells, used for memory recall and navigation when the spatial view details are obscured. Inferior temporal object "what" inputs and orbitofrontal cortex reward inputs connect to the human hippocampal system, and in macaques can be associated in the hippocampus with spatial view cell "where" representations to implement episodic memory. Hippocampal spatial view cells also provide a basis for navigation to a series of viewed landmarks, with the orbitofrontal cortex reward inputs to the hippocampus providing the goals for navigation, which can then be implemented by hippocampal connectivity in humans to parietal cortex regions involved in visuomotor actions in space. The presence of foveate vision and the highly developed temporal lobe for object and scene processing in primates including humans provide a basis for hippocampal spatial view cells to be key to understanding episodic memory in the primate and human hippocampus, and the roles of this system in primate including human navigation.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational NeuroscienceOxfordUK
- Department of Computer ScienceUniversity of WarwickCoventryUK
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14
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Velasquez F, Dickson C, Kloc ML, Schneur CA, Barry JM, Holmes GL. Optogenetic modulation of hippocampal oscillations ameliorates spatial cognition and hippocampal dysrhythmia following early-life seizures. Neurobiol Dis 2023; 178:106021. [PMID: 36720444 DOI: 10.1016/j.nbd.2023.106021] [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: 09/14/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 01/30/2023] Open
Abstract
There is increasing human and animal evidence that brain oscillations play a critical role in the development of spatial cognition. In rat pups, disruption of hippocampal rhythms via optogenetic stimulation during the critical period for memory development impairs spatial cognition. Early-life seizures are associated with long-term deficits in spatial cognition and aberrant hippocampal oscillatory activity. Here we asked whether modulation of hippocampal rhythms following early-life seizures can reverse or improve hippocampal connectivity and spatial cognition. We used optogenetic stimulation of the medial septum to induce physiological 7 Hz theta oscillations in the hippocampus during the critical period of spatial cognition following early-life seizures. Optogenetic stimulation of the medial septum in control and rats subjected to early-life seizures resulted in precisely regulated frequency-matched hippocampal oscillations. Rat pups receiving active blue light stimulation performed better than the rats receiving inert yellow light in a test of spatial cognition. The improvement in spatial cognition in these rats was associated with a faster theta frequency and higher theta power, coherence and phase locking value in the hippocampus than rats with early-life seizures receiving inert yellow light. These findings indicate that following early life seizures, modification of hippocampal rhythms may be a potential novel therapeutic modality.
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Affiliation(s)
- Francisco Velasquez
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Conor Dickson
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Michelle L Kloc
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Carmel A Schneur
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Jeremy M Barry
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Gregory L Holmes
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA.
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15
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Morris G, Derdikman D. The chicken and egg problem of grid cells and place cells. Trends Cogn Sci 2023; 27:125-138. [PMID: 36437188 DOI: 10.1016/j.tics.2022.11.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 11/26/2022]
Abstract
Place cells and grid cells are major building blocks of the hippocampal cognitive map. The prominent forward model postulates that grid-cell modules are generated by a continuous attractor network; that a velocity signal evoked during locomotion moves entorhinal activity bumps; and that place-cell activity constitutes summation of entorhinal grid-cell modules. Experimental data support the first postulate, but not the latter two. Several families of solutions that depart from these postulates have been put forward. We suggest a modified model (spatial modulation continuous attractor network; SCAN), whereby place cells are generated from spatially selective nongrid cells. Locomotion causes these cells to move the hippocampal activity bump, leading to movement of the entorhinal manifolds. Such inversion accords with the shift of hippocampal thought from navigation to more abstract functions.
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Affiliation(s)
- Genela Morris
- Department of Neuroscience, Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa, Israel; Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
| | - Dori Derdikman
- Department of Neuroscience, Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa, Israel.
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16
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Qin S, Farashahi S, Lipshutz D, Sengupta AM, Chklovskii DB, Pehlevan C. Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning. Nat Neurosci 2023; 26:339-349. [PMID: 36635497 DOI: 10.1038/s41593-022-01225-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 10/28/2022] [Indexed: 01/13/2023]
Abstract
Recent experiments have revealed that neural population codes in many brain areas continuously change even when animals have fully learned and stably perform their tasks. This representational 'drift' naturally leads to questions about its causes, dynamics and functions. Here we explore the hypothesis that neural representations optimize a representational objective with a degenerate solution space, and noisy synaptic updates drive the network to explore this (near-)optimal space causing representational drift. We illustrate this idea and explore its consequences in simple, biologically plausible Hebbian/anti-Hebbian network models of representation learning. We find that the drifting receptive fields of individual neurons can be characterized by a coordinated random walk, with effective diffusion constants depending on various parameters such as learning rate, noise amplitude and input statistics. Despite such drift, the representational similarity of population codes is stable over time. Our model recapitulates experimental observations in the hippocampus and posterior parietal cortex and makes testable predictions that can be probed in future experiments.
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Affiliation(s)
- Shanshan Qin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Shiva Farashahi
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
| | - David Lipshutz
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
| | - Anirvan M Sengupta
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
- Department of Physics and Astronomy, Rutgers University, New Brunswick, NJ, USA
| | - Dmitri B Chklovskii
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
- NYU Langone Medical Center, New York, NY, USA
| | - Cengiz Pehlevan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
- Center for Brain Science, Harvard University, Cambridge, MA, USA.
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17
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Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task. iScience 2023; 26:106026. [PMID: 36818295 PMCID: PMC9929609 DOI: 10.1016/j.isci.2023.106026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 11/18/2022] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
In recent years, the biological underpinnings of adaptive learning have been modeled, leading to faster model convergence and various behavioral benefits in tasks including spatial navigation and cue-reward association. Furthermore, studies have investigated how the neuromodulatory system, a major driver of synaptic plasticity and state-dependent changes in the brain neuronal activities, plays a role in training deep neural networks (DNNs). In this study, we extended previous studies on neuromodulation-inspired DNNs and explored the effects of neuromodulatory components on learning and single unit activities in a spatial learning task. Under the multiscale neuromodulatory framework, plastic components, dropout probability modulation, and learning rate decay were added to the single unit, layer, and whole network levels of DNN models, respectively. We observed behavioral benefits including faster learning and smaller error of ambulation. We then concluded that neuromodulatory components can affect learning trajectories, outcomes, and single unit activities, in a component- and hyperparameter-dependent manner.
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18
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Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model. Sci Rep 2022; 12:21443. [PMID: 36509873 PMCID: PMC9744848 DOI: 10.1038/s41598-022-25863-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal's exploration of a square arena. The grid cell model processed the animal's velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal's position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal's current location contributed more to the error reduction than remote place fields. Place cells' fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.
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19
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Safron A. Integrated world modeling theory expanded: Implications for the future of consciousness. Front Comput Neurosci 2022; 16:642397. [PMID: 36507308 PMCID: PMC9730424 DOI: 10.3389/fncom.2022.642397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 10/24/2022] [Indexed: 11/27/2022] Open
Abstract
Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT's integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the "Bayesian blur problem," or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated "unlimited associative learning." While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.
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Affiliation(s)
- Adam Safron
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Center for Psychedelic and Consciousness Research, Baltimore, MD, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Institute for Advanced Consciousness Studies (IACS), Santa Monica, CA, United States
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20
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Khona M, Fiete IR. Attractor and integrator networks in the brain. Nat Rev Neurosci 2022; 23:744-766. [DOI: 10.1038/s41583-022-00642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2022] [Indexed: 11/06/2022]
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21
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Aziz A, Sreeharsha PSS, Natesh R, Chakravarthy VS. An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information. Hippocampus 2022; 32:716-730. [DOI: 10.1002/hipo.23461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 07/10/2022] [Accepted: 07/16/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Azra Aziz
- Computational Neuroscience Lab Indian Institute of Technology Madras Chennai India
| | | | - Rohan Natesh
- Department of Electronics Engineering Indian Institute of Technology (BHU) Varanasi India
| | - Vaddadhi S. Chakravarthy
- Computational Neuroscience Lab Indian Institute of Technology Madras Chennai India
- Center for Complex Systems and Dynamics Indian Institute of Technology Madras Chennai India
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22
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Spalla D, Treves A, Boccara CN. Angular and linear speed cells in the parahippocampal circuits. Nat Commun 2022; 13:1907. [PMID: 35393433 PMCID: PMC8991198 DOI: 10.1038/s41467-022-29583-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 03/08/2022] [Indexed: 11/09/2022] Open
Abstract
An essential role of the hippocampal region is to integrate information to compute and update representations. How this transpires is highly debated. Many theories hinge on the integration of self-motion signals and the existence of continuous attractor networks (CAN). CAN models hypothesise that neurons coding for navigational correlates – such as position and direction – receive inputs from cells conjunctively coding for position, direction, and self-motion. As yet, very little data exist on such conjunctive coding in the hippocampal region. Here, we report neurons coding for angular and linear velocity, uniformly distributed across the medial entorhinal cortex (MEC), the presubiculum and the parasubiculum, except for MEC layer II. Self-motion neurons often conjunctively encoded position and/or direction, yet lacked a structured organisation. These results offer insights as to how linear/angular speed – derivative in time of position/direction – may allow the updating of spatial representations, possibly uncovering a generalised algorithm to update any representation. It remains unclear how the hippocampal region integrates position and self-motion information to update spatial representations. Here, the authors report grid and head direction cells as well as cells encoding self-motion parameters such as angular head velocity and speed, and find conjunctive representations of these different parameters.
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Affiliation(s)
| | | | - Charlotte N Boccara
- University of Oslo, Faculty of Medicine, IMB, Sognsvannsveien 9 Domus Medica, 0372, Oslo, Norway.
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23
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Tukker JJ, Beed P, Brecht M, Kempter R, Moser EI, Schmitz D. Microcircuits for spatial coding in the medial entorhinal cortex. Physiol Rev 2022; 102:653-688. [PMID: 34254836 PMCID: PMC8759973 DOI: 10.1152/physrev.00042.2020] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The hippocampal formation is critically involved in learning and memory and contains a large proportion of neurons encoding aspects of the organism's spatial surroundings. In the medial entorhinal cortex (MEC), this includes grid cells with their distinctive hexagonal firing fields as well as a host of other functionally defined cell types including head direction cells, speed cells, border cells, and object-vector cells. Such spatial coding emerges from the processing of external inputs by local microcircuits. However, it remains unclear exactly how local microcircuits and their dynamics within the MEC contribute to spatial discharge patterns. In this review we focus on recent investigations of intrinsic MEC connectivity, which have started to describe and quantify both excitatory and inhibitory wiring in the superficial layers of the MEC. Although the picture is far from complete, it appears that these layers contain robust recurrent connectivity that could sustain the attractor dynamics posited to underlie grid pattern formation. These findings pave the way to a deeper understanding of the mechanisms underlying spatial navigation and memory.
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Affiliation(s)
- John J Tukker
- German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
| | - Prateep Beed
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humbold-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Brecht
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany
- Neurocure Cluster of Excellence, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edvard I Moser
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway
| | - Dietmar Schmitz
- German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humbold-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Neurocure Cluster of Excellence, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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24
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25
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Gardner RJ, Hermansen E, Pachitariu M, Burak Y, Baas NA, Dunn BA, Moser MB, Moser EI. Toroidal topology of population activity in grid cells. Nature 2022; 602:123-128. [PMID: 35022611 PMCID: PMC8810387 DOI: 10.1038/s41586-021-04268-7] [Citation(s) in RCA: 120] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 11/19/2021] [Indexed: 01/24/2023]
Abstract
The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment1. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations2, and are organized in modules3 that collectively form a population code for the animal's allocentric position1. The invariance of the correlation structure of this population code across environments4,5 and behavioural states6,7, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,8-11. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models12. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.
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Affiliation(s)
- Richard J Gardner
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Erik Hermansen
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Yoram Burak
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nils A Baas
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Benjamin A Dunn
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway.
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
| | - May-Britt Moser
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway
| | - Edvard I Moser
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway.
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26
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Soldatkina O, Schönsberg F, Treves A. Challenges for Place and Grid Cell Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:285-312. [DOI: 10.1007/978-3-030-89439-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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27
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Xiao ZC, Lin KK, Young LS. A data-informed mean-field approach to mapping of cortical parameter landscapes. PLoS Comput Biol 2021; 17:e1009718. [PMID: 34941863 PMCID: PMC8741023 DOI: 10.1371/journal.pcbi.1009718] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 01/07/2022] [Accepted: 12/02/2021] [Indexed: 11/19/2022] Open
Abstract
Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.
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Affiliation(s)
- Zhuo-Cheng Xiao
- Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
| | - Kevin K. Lin
- Department of Mathematics, University of Arizona, Tucson, Arizona, United States of America
| | - Lai-Sang Young
- Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
- Institute for Advanced Study, Princeton, New Jersey, United States of America
- * E-mail:
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28
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Zhi Y, Cox D. Neurodegenerative damage reduces firing coherence in a continuous attractor model of grid cells. Phys Rev E 2021; 104:044414. [PMID: 34781544 DOI: 10.1103/physreve.104.044414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 08/25/2021] [Indexed: 11/07/2022]
Abstract
Grid cells in the dorsolateral band of the medial entorhinal cortex (dMEC) display strikingly regular periodic firing patterns on a lattice of positions in two-dimensional (2D) space. This helps animals to encode relative spatial location without reference to external cues. The dMEC is damaged in the early stages of Alzheimer's disease, which affects navigation ability of a disease victim, reducing the synaptic density of neurons in the network. Within an established two-dimensional continuous attractor neural network model of grid cell activity, we introduce neural sheet damage parametrized by radius and by the strength of the synaptic output for neurons in the damaged region. The mean proportionality of the grid field flow rate in the dMEC to the velocity of the model animal is maintained, but there is a broadened distribution of flow rates in the damaged case. This flow-rate-to-velocity proportionality is essential to establish coherent grid firing fields for individual grid cells for a roaming animal. When we examine the coherence of the grid cell firing field by studying Bragg peaks of the Fourier transformed lattice firing field intensity in both damaged and undamaged regions, we find that, for a wide range of damage radius and reduced synaptic strength, for undamaged model grid cells there is an incoherent firing field structure with only a single central peak. In the radius-damage plane this is adjacent to narrow bands of striped lattices (two additional Bragg peaks), which abut an orthorhombic pattern (four additional Bragg peaks), that abuts the undamaged hexagonal region (six additional Bragg peaks). Within the damaged region, grid cells show no Bragg peaks outside the central one, which shows reduced intensity with increasing damage, and outside the damaged region the central Bragg peak strength is largely unaffected. There is a reentrant region of normal grid firing fields for very large damage area. We anticipate that the modified grid cell behavior can be observed in noninvasive functional magnetic resonance imaging (fMRI) of the dMEC.
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Affiliation(s)
- Yuduo Zhi
- Physics Department, University of California, Davis, California 95616, USA
| | - Daniel Cox
- Physics Department, University of California, Davis, California 95616, USA
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29
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Gong Z, Yu F. A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes. Front Comput Neurosci 2021; 15:739515. [PMID: 34630061 PMCID: PMC8493087 DOI: 10.3389/fncom.2021.739515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/20/2021] [Indexed: 11/30/2022] Open
Abstract
Grid cells are crucial in path integration and representation of the external world. The spikes of grid cells spatially form clusters called grid fields, which encode important information about allocentric positions. To decode the information, studying the spatial structures of grid fields is a key task for both experimenters and theorists. Experiments reveal that grid fields form hexagonal lattice during planar navigation, and are anisotropic beyond planar navigation. During volumetric navigation, they lose global order but possess local order. How grid cells form different field structures behind these different navigation modes remains an open theoretical question. However, to date, few models connect to the latest discoveries and explain the formation of various grid field structures. To fill in this gap, we propose an interpretive plane-dependent model of three-dimensional (3D) grid cells for representing both two-dimensional (2D) and 3D space. The model first evaluates motion with respect to planes, such as the planes animals stand on and the tangent planes of the motion manifold. Projection of the motion onto the planes leads to anisotropy, and error in the perception of planes degrades grid field regularity. A training-free recurrent neural network (RNN) then maps the processed motion information to grid fields. We verify that our model can generate regular and anisotropic grid fields, as well as grid fields with merely local order; our model is also compatible with mode switching. Furthermore, simulations predict that the degradation of grid field regularity is inversely proportional to the interval between two consecutive perceptions of planes. In conclusion, our model is one of the few pioneers that address grid field structures in a general case. Compared to the other pioneer models, our theory argues that the anisotropy and loss of global order result from the uncertain perception of planes rather than insufficient training.
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Affiliation(s)
- Ziyi Gong
- Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China.,Department of Neurobiology, School of Medicine, Duke University, Durham, NC, United States
| | - Fangwen Yu
- Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China
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30
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Rueckemann JW, Sosa M, Giocomo LM, Buffalo EA. The grid code for ordered experience. Nat Rev Neurosci 2021; 22:637-649. [PMID: 34453151 PMCID: PMC9371942 DOI: 10.1038/s41583-021-00499-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
Entorhinal cortical grid cells fire in a periodic pattern that tiles space, which is suggestive of a spatial coordinate system. However, irregularities in the grid pattern as well as responses of grid cells in contexts other than spatial navigation have presented a challenge to existing models of entorhinal function. In this Perspective, we propose that hippocampal input provides a key informative drive to the grid network in both spatial and non-spatial circumstances, particularly around salient events. We build on previous models in which neural activity propagates through the entorhinal-hippocampal network in time. This temporal contiguity in network activity points to temporal order as a necessary characteristic of representations generated by the hippocampal formation. We advocate that interactions in the entorhinal-hippocampal loop build a topological representation that is rooted in the temporal order of experience. In this way, the structure of grid cell firing supports a learned topology rather than a rigid coordinate frame that is bound to measurements of the physical world.
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Affiliation(s)
- Jon W Rueckemann
- Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | - Marielena Sosa
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Elizabeth A Buffalo
- Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA.
- Washington National Primate Research Center, Seattle, WA, USA.
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31
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Yan Y, Burgess N, Bicanski A. A model of head direction and landmark coding in complex environments. PLoS Comput Biol 2021; 17:e1009434. [PMID: 34570749 PMCID: PMC8496825 DOI: 10.1371/journal.pcbi.1009434] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 10/07/2021] [Accepted: 09/08/2021] [Indexed: 01/27/2023] Open
Abstract
Environmental information is required to stabilize estimates of head direction (HD) based on angular path integration. However, it is unclear how this happens in real-world (visually complex) environments. We present a computational model of how visual feedback can stabilize HD information in environments that contain multiple cues of varying stability and directional specificity. We show how combinations of feature-specific visual inputs can generate a stable unimodal landmark bearing signal, even in the presence of multiple cues and ambiguous directional specificity. This signal is associated with the retrosplenial HD signal (inherited from thalamic HD cells) and conveys feedback to the subcortical HD circuitry. The model predicts neurons with a unimodal encoding of the egocentric orientation of the array of landmarks, rather than any one particular landmark. The relationship between these abstract landmark bearing neurons and head direction cells is reminiscent of the relationship between place cells and grid cells. Their unimodal encoding is formed from visual inputs via a modified version of Oja's Subspace Algorithm. The rule allows the landmark bearing signal to disconnect from directionally unstable or ephemeral cues, incorporate newly added stable cues, support orientation across many different environments (high memory capacity), and is consistent with recent empirical findings on bidirectional HD firing reported in the retrosplenial cortex. Our account of visual feedback for HD stabilization provides a novel perspective on neural mechanisms of spatial navigation within richer sensory environments, and makes experimentally testable predictions.
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Affiliation(s)
- Yijia Yan
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Neil Burgess
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Andrej Bicanski
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- School of Psychology, Newcastle University, Newcastle upon Tyne, United Kingdom
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32
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Locally ordered representation of 3D space in the entorhinal cortex. Nature 2021; 596:404-409. [PMID: 34381211 DOI: 10.1038/s41586-021-03783-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 06/29/2021] [Indexed: 02/07/2023]
Abstract
As animals navigate on a two-dimensional surface, neurons in the medial entorhinal cortex (MEC) known as grid cells are activated when the animal passes through multiple locations (firing fields) arranged in a hexagonal lattice that tiles the locomotion surface1. However, although our world is three-dimensional, it is unclear how the MEC represents 3D space2. Here we recorded from MEC cells in freely flying bats and identified several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing fields. Many of these multifield neurons were 3D grid cells, whose neighbouring fields were separated by a characteristic distance-forming a local order-but lacked any global lattice arrangement of the fields. Thus, whereas 2D grid cells form a global lattice-characterized by both local and global order-3D grid cells exhibited only local order, creating a locally ordered metric for space. We modelled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D, thereby describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.
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33
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Mittal D, Narayanan R. Resonating neurons stabilize heterogeneous grid-cell networks. eLife 2021; 10:66804. [PMID: 34328415 PMCID: PMC8357421 DOI: 10.7554/elife.66804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/29/2021] [Indexed: 01/02/2023] Open
Abstract
A central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. Here, we investigated the impact of distinct forms of biological heterogeneities on the stability of a two-dimensional continuous attractor network (CAN) implicated in grid-patterned activity generation. We show that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in low-frequency neural activity. We postulated that targeted suppression of low-frequency perturbations could ameliorate heterogeneity-induced disruptions of grid-patterned activity. To test this, we introduced intrinsic resonance, a physiological mechanism to suppress low-frequency activity, either by adding an additional high-pass filter (phenomenological) or by incorporating a slow negative feedback loop (mechanistic) into our model neurons. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing. We found CAN models with mechanistic resonators to be more effective in targeted suppression of low-frequency activity, with the slow kinetics of the negative feedback loop essential in stabilizing these networks. As low-frequency perturbations (1/f noise) are pervasive across biological systems, our analyses suggest a universal role for mechanisms that suppress low-frequency activity in stabilizing heterogeneous biological networks.
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Affiliation(s)
- Divyansh Mittal
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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34
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Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding. eNeuro 2021; 8:ENEURO.0557-20.2021. [PMID: 34162691 PMCID: PMC8266216 DOI: 10.1523/eneuro.0557-20.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 12/03/2022] Open
Abstract
Cells in the entorhinal cortex (EC) contain rich spatial information and project strongly to the hippocampus where a cognitive map is supposedly created. These cells range from cells with structured spatial selectivity, such as grid cells in the medial EC (MEC) that are selective to an array of spatial locations that form a hexagonal grid, to weakly spatial cells, such as non-grid cells in the MEC and lateral EC (LEC) that contain spatial information but have no structured spatial selectivity. However, in a small environment, place cells in the hippocampus are generally selective to a single location of the environment, while granule cells in the dentate gyrus of the hippocampus have multiple discrete firing locations but lack spatial periodicity. Given the anatomic connection from the EC to the hippocampus, how the hippocampus retrieves information from upstream EC remains unclear. Here, we propose a unified learning model that can describe the spatial tuning properties of both hippocampal place cells and dentate gyrus granule cells based on non-negative sparse coding from EC inputs. Sparse coding plays an important role in many cortical areas and is proposed here to have a key role in the hippocampus. Our results show that the hexagonal patterns of MEC grid cells with various orientations, grid spacings and phases are necessary for the model to learn different place cells that efficiently tile the entire spatial environment. However, if there is a lack of diversity in any grid parameters or a lack of hippocampal cells in the network, this will lead to the emergence of hippocampal cells that have multiple firing locations. More surprisingly, the model can also learn hippocampal place cells even when weakly spatial cells, instead of grid cells, are used as the input to the hippocampus. This work suggests that sparse coding may be one of the underlying organizing principles for the navigational system of the brain.
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35
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Place cells and geometry lead to a flexible grid pattern. J Comput Neurosci 2021; 49:441-452. [PMID: 34125337 DOI: 10.1007/s10827-021-00794-5] [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: 02/13/2021] [Revised: 05/04/2021] [Accepted: 06/03/2021] [Indexed: 10/21/2022]
Abstract
Place cells and grid cells are important neurons involved in spatial navigation in the mammalian brain. Grid cells are believed to play an important role in forming a cognitive map of the environment. Experimental observations in recent years showed that the grid pattern is not invariant but is influenced by the shape of the spatial environment. However, the cause of this deformation remains elusive. Here, we focused on the functional interactions between place cells and grid cells, utilizing the information of location relationships between the firing fields of place cells to optimize the previous grid cell feedforward generation model and expand its application to more complex environmental scenarios. Not only was the regular equilateral triangle periodic firing field structure of the grid cells reproduced, but the expected results were consistent with the experiment for the environment with various complex boundary shapes and environmental deformation. Even in the field of three-dimensional spatial grid patterns, forward-looking predictions have been made. This provides a possible model explanation for how the coupling of grid cells and place cells adapt to the diversity of the external environment to deepen our understanding of the neural basis for constructing cognitive maps.
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36
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Yim MY, Sadun LA, Fiete IR, Taillefumier T. Place-cell capacity and volatility with grid-like inputs. eLife 2021; 10:e62702. [PMID: 34028354 PMCID: PMC8294848 DOI: 10.7554/elife.62702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 04/28/2021] [Indexed: 01/07/2023] Open
Abstract
What factors constrain the arrangement of the multiple fields of a place cell? By modeling place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically enumerate a place cell's repertoire - how many field arrangements it can realize without external cues while its grid inputs are unique - and derive its capacity - the spatial range over which it can achieve any field arrangement. We show that the repertoire is very large and relatively noise-robust. However, the repertoire is a vanishing fraction of all arrangements, while capacity scales only as the sum of the grid periods so field arrangements are constrained over larger distances. Thus, grid-driven place field arrangements define a large response scaffold that is strongly constrained by its structured inputs. Finally, we show that altering grid-place weights to generate an arbitrary new place field strongly affects existing arrangements, which could explain the volatility of the place code.
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Affiliation(s)
- Man Yi Yim
- Center for Theoretical and Computational Neuroscience, University of TexasAustinUnited States
- Department of Neuroscience, University of TexasAustinUnited States
- Department of Brain and Cognitive Sciences and McGovern Institute, MITAustinUnited States
| | - Lorenzo A Sadun
- Department of Mathematics and Neuroscience, The University of TexasAustinUnited States
| | - Ila R Fiete
- Center for Theoretical and Computational Neuroscience, University of TexasAustinUnited States
- Department of Brain and Cognitive Sciences and McGovern Institute, MITAustinUnited States
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, University of TexasAustinUnited States
- Department of Neuroscience, University of TexasAustinUnited States
- Department of Mathematics and Neuroscience, The University of TexasAustinUnited States
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37
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Rolls ET. Neurons including hippocampal spatial view cells, and navigation in primates including humans. Hippocampus 2021; 31:593-611. [PMID: 33760309 DOI: 10.1002/hipo.23324] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/01/2021] [Accepted: 03/13/2021] [Indexed: 01/11/2023]
Abstract
A new theory is proposed of mechanisms of navigation in primates including humans in which spatial view cells found in the primate hippocampus and parahippocampal gyrus are used to guide the individual from landmark to landmark. The navigation involves approach to each landmark in turn (taxis), using spatial view cells to identify the next landmark in the sequence, and does not require a topological map. Two other cell types found in primates, whole body motion cells, and head direction cells, can be utilized in the spatial view cell navigational mechanism, but are not essential. If the landmarks become obscured, then the spatial view representations can be updated by self-motion (idiothetic) path integration using spatial coordinate transform mechanisms in the primate dorsal visual system to transform from egocentric to allocentric spatial view coordinates. A continuous attractor network or time cells or working memory is used in this approach to navigation to encode and recall the spatial view sequences involved. I also propose how navigation can be performed using a further type of neuron found in primates, allocentric-bearing-to-a-landmark neurons, in which changes of direction are made when a landmark reaches a particular allocentric bearing. This is useful if a landmark cannot be approached. The theories are made explicit in models of navigation, which are then illustrated by computer simulations. These types of navigation are contrasted with triangulation, which requires a topological map. It is proposed that the first strategy utilizing spatial view cells is used frequently in humans, and is relatively simple because primates have spatial view neurons that respond allocentrically to locations in spatial scenes. An advantage of this approach to navigation is that hippocampal spatial view neurons are also useful for episodic memory, and for imagery.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry, UK
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38
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Christensen AC, Lensjø KK, Lepperød ME, Dragly SA, Sutterud H, Blackstad JS, Fyhn M, Hafting T. Perineuronal nets stabilize the grid cell network. Nat Commun 2021; 12:253. [PMID: 33431847 PMCID: PMC7801665 DOI: 10.1038/s41467-020-20241-w] [Citation(s) in RCA: 269] [Impact Index Per Article: 89.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 11/16/2020] [Indexed: 11/29/2022] Open
Abstract
Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells' ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.
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Affiliation(s)
- Ane Charlotte Christensen
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Kristian Kinden Lensjø
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Mikkel Elle Lepperød
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Svenn-Arne Dragly
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Halvard Sutterud
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Jan Sigurd Blackstad
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Marianne Fyhn
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Torkel Hafting
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.
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39
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D'Albis T, Kempter R. Recurrent amplification of grid-cell activity. Hippocampus 2020; 30:1268-1297. [PMID: 33022854 DOI: 10.1002/hipo.23254] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 06/18/2020] [Accepted: 07/25/2020] [Indexed: 11/07/2022]
Abstract
High-level cognitive abilities such as navigation and spatial memory are thought to rely on the activity of grid cells in the medial entorhinal cortex (MEC), which encode the animal's position in space with periodic triangular patterns. Yet the neural mechanisms that underlie grid-cell activity are still unknown. Recent in vitro and in vivo experiments indicate that grid cells are embedded in highly structured recurrent networks. But how could recurrent connectivity become structured during development? And what is the functional role of these connections? With mathematical modeling and simulations, we show that recurrent circuits in the MEC could emerge under the supervision of weakly grid-tuned feedforward inputs. We demonstrate that a learned excitatory connectivity could amplify grid patterns when the feedforward sensory inputs are available and sustain attractor states when the sensory cues are lost. Finally, we propose a Fourier-based measure to quantify the spatial periodicity of grid patterns: the grid-tuning index.
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Affiliation(s)
- Tiziano D'Albis
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany.,Einstein Center for Neurosciences, Berlin, Germany
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40
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Vinepinsky E, Perchik S, Segev R. A Generalized Linear Model of a Navigation Network. Front Neural Circuits 2020; 14:56. [PMID: 33013326 PMCID: PMC7509173 DOI: 10.3389/fncir.2020.00056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/28/2020] [Indexed: 11/22/2022] Open
Abstract
Navigation by mammals is believed to rely on a network of neurons in the hippocampal formation, which includes the hippocampus, the medial entorhinal cortex (MEC), and additional nearby regions. Neurons in these regions represent spatial information by tuning to the position, orientation, and speed of the animal in the form of head direction cells, speed cells, grid cells, border cells, and unclassified spatially modulated cells. While the properties of single cells are well studied, little is known about the functional structure of the network in the MEC. Here, we use a generalized linear model to study the network of spatially modulated cells in the MEC. We found connectivity patterns between all spatially encoding cells and not only grid cells. In addition, the neurons’ past activity contributed to the overall activity patterns. Finally, position-modulated cells and head direction cells differed in the dependence of the activity on the history. Our results indicate that MEC neurons form a local interacting network to support spatial information representations and suggest an explanation for their complex temporal properties.
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Affiliation(s)
- Ehud Vinepinsky
- Department of Life Sciences, Ben Gurion University of the Negev, Beersheba, Israel.,Zlotowski Center for Neuroscience, Ben Gurion University of the Negev, Beersheba, Israel
| | - Shay Perchik
- Zlotowski Center for Neuroscience, Ben Gurion University of the Negev, Beersheba, Israel.,Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, Beersheba, Israel
| | - Ronen Segev
- Department of Life Sciences, Ben Gurion University of the Negev, Beersheba, Israel.,Zlotowski Center for Neuroscience, Ben Gurion University of the Negev, Beersheba, Israel.,Department of Biomedical Engineering, Ben Gurion University of the Negev, Beersheba, Israel
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41
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The Generation of Time in the Hippocampal Memory System. Cell Rep 2020; 28:1649-1658.e6. [PMID: 31412236 DOI: 10.1016/j.celrep.2019.07.042] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/30/2019] [Accepted: 07/14/2019] [Indexed: 11/21/2022] Open
Abstract
We propose that ramping time cells in the lateral entorhinal cortex can be produced by synaptic adaptation and demonstrate this in an integrate-and-fire attractor network model. We propose that competitive networks in the hippocampal system can convert these entorhinal ramping cells into hippocampal time cells and demonstrate this in a competitive network. We propose that this conversion is necessary to provide orthogonal hippocampal time representations to encode the temporal sequence of events in hippocampal episodic memory, and we support that with analytic arguments. We demonstrate that this processing can produce hippocampal neuronal ensembles that not only show replay of the sequence later on, but can also do this in reverse order in reverse replay. This research addresses a major issue in neuroscience: the mechanisms by which time is encoded in the brain and how the time representations are then useful in the hippocampal memory of events and their order.
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42
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Kloc ML, Velasquez F, Niedecker RW, Barry JM, Holmes GL. Disruption of hippocampal rhythms via optogenetic stimulation during the critical period for memory development impairs spatial cognition. Brain Stimul 2020; 13:1535-1547. [PMID: 32871261 DOI: 10.1016/j.brs.2020.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/14/2020] [Accepted: 08/18/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Hippocampal oscillations play a critical role in the ontogeny of allocentric memory in rodents. During the critical period for memory development, hippocampal theta is the driving force behind the temporal coordination of neuronal ensembles underpinning spatial memory. While known that hippocampal oscillations are necessary for normal spatial cognition, whether disrupted hippocampal oscillatory activity during the critical period impairs long-term spatial memory is unknown. Here we investigated whether disruption of normal hippocampal rhythms during the critical period have enduring effects on allocentric memory in rodents. OBJECTIVE/HYPOTHESIS We hypothesized that disruption of hippocampal oscillations via artificial regulation of the medial septum during the critical period for memory development results in long-standing deficits in spatial cognition. METHODS After demonstrating that pan-neuronal medial septum (MS) optogenetic stimulation (465 nm activated) regulated hippocampal oscillations in weanling rats we used a random pattern of stimulation frequencies to disrupt hippocampal theta rhythms for either 1Hr or 5hr a day between postnatal (P) days 21-25. Non-stimulated and yellow light-stimulated (590 nm) rats served as controls. At P50-60 all rats were tested for spatial cognition in the active avoidance task. Rats were then sacrificed, and the MS and hippocampus assessed for cell loss. Power spectrum density of the MS and hippocampus, coherences and voltage correlations between MS and hippocampus were evaluated at baseline for a range of stimulation frequencies from 0.5 to 110 Hz and during disruptive hippocampal stimulation. Unpaired t-tests and ANOVA were used to compare oscillatory parameters, behavior and cell density in all animals. RESULTS Non-selective optogenetic stimulation of the MS in P21 rats resulted in precise regulation of hippocampal oscillations with 1:1 entrainment between stimulation frequency (0.5-110 Hz) and hippocampal local field potentials. Across bandwidths MS stimulation increased power, coherence and voltage correlation at all frequencies whereas the disruptive stimulation increased power and reduced coherence and voltage correlations with most statistical measures highly significant (p < 0.001, following correction for false detection). Rats receiving disruptive hippocampal stimulation during the critical period for memory development for either 1Hr or 5hr had marked impairment in spatial learning as measured in active avoidance test compared to non-stimulated or yellow light-control rats (p < 0.001). No cell loss was measured between the blue-stimulated and non-stimulated or yellow light-stimulated controls in either the MS or hippocampus. CONCLUSION The results demonstrated that robust regulation of hippocampal oscillations can be achieved with non-selective optogenetic stimulation of the MS in rat pups. A disruptive hippocampal stimulation protocol, which markedly increases power and reduces coherence and voltage correlations between the MS and hippocampus during the critical period of memory development, results in long-standing spatial cognitive deficits. This spatial cognitive impairment is not a result of optogenetic stimulation-induced cell loss.
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Affiliation(s)
- Michelle L Kloc
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Francisco Velasquez
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Rhys W Niedecker
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Jeremy M Barry
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Gregory L Holmes
- Epilepsy Development and Cognition Group, Department of Neurological Sciences, University of Vermont, Larner College of Medicine, Burlington, VT, USA.
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43
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Agmon H, Burak Y. A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial remapping and grid cell field-to-field variability. eLife 2020; 9:56894. [PMID: 32779570 PMCID: PMC7447444 DOI: 10.7554/elife.56894] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 08/07/2020] [Indexed: 01/04/2023] Open
Abstract
The representation of position in the mammalian brain is distributed across multiple neural populations. Grid cell modules in the medial entorhinal cortex (MEC) express activity patterns that span a low-dimensional manifold which remains stable across different environments. In contrast, the activity patterns of hippocampal place cells span distinct low-dimensional manifolds in different environments. It is unknown how these multiple representations of position are coordinated. Here, we develop a theory of joint attractor dynamics in the hippocampus and the MEC. We show that the system exhibits a coordinated, joint representation of position across multiple environments, consistent with global remapping in place cells and grid cells. In addition, our model accounts for recent experimental observations that lack a mechanistic explanation: variability in the firing rate of single grid cells across firing fields, and artificial remapping of place cells under depolarization, but not under hyperpolarization, of layer II stellate cells of the MEC.
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Affiliation(s)
- Haggai Agmon
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yoram Burak
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel
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44
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Kaya Z, Soltanipour M, Treves A. Non-hexagonal neural dynamics in vowel space. AIMS Neurosci 2020; 7:275-298. [PMID: 32995486 PMCID: PMC7519971 DOI: 10.3934/neuroscience.2020015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/27/2020] [Indexed: 12/02/2022] Open
Abstract
Are the grid cells discovered in rodents relevant to human cognition? Following up on two seminal studies by others, we aimed to check whether an approximate 6-fold, grid-like symmetry shows up in the cortical activity of humans who "navigate" between vowels, given that vowel space can be approximated with a continuous trapezoidal 2D manifold, spanned by the first and second formant frequencies. We created 30 vowel trajectories in the assumedly flat central portion of the trapezoid. Each of these trajectories had a duration of 240 milliseconds, with a steady start and end point on the perimeter of a "wheel". We hypothesized that if the neural representation of this "box" is similar to that of rodent grid units, there should be an at least partial hexagonal (6-fold) symmetry in the EEG response of participants who navigate it. We have not found any dominant n-fold symmetry, however, but instead, using PCAs, we find indications that the vowel representation may reflect phonetic features, as positioned on the vowel manifold. The suggestion, therefore, is that vowels are encoded in relation to their salient sensory-perceptual variables, and are not assigned to arbitrary grid-like abstract maps. Finally, we explored the relationship between the first PCA eigenvector and putative vowel attractors for native Italian speakers, who served as the subjects in our study.
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Affiliation(s)
- Zeynep Kaya
- SISSA–Cognitive Neuroscience, via Bonomea 265, 34136 Trieste, Italy
| | | | - Alessandro Treves
- SISSA–Cognitive Neuroscience, via Bonomea 265, 34136 Trieste, Italy
- NTNU–Centre for Neural Computation, Trondheim, Norway
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45
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A computational model for grid maps in neural populations. J Comput Neurosci 2020; 48:149-159. [PMID: 32125562 DOI: 10.1007/s10827-020-00742-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 02/06/2020] [Accepted: 02/11/2020] [Indexed: 10/24/2022]
Abstract
Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain. In this work we introduce a novel theoretical and algorithmic framework able to explain the optimality of hexagonal grid-like response patterns. We show that this pattern is a result of minimal variance encoding of neurons together with maximal robustness to neurons' noise and minimal number of encoding neurons. The novelty lies in the formulation of the encoding problem considering neurons as an overcomplete basis (a frame) where the position information is encoded. Through the modern Frame Theory language, specifically that of tight and equiangular frames, we provide new insights about the optimality of hexagonal grid receptive fields. The proposed model is based on the well-accepted and tested hypothesis of Hebbian learning, providing a simplified cortical-based framework that does not require the presence of velocity-driven oscillations (oscillatory model) or translational symmetries in the synaptic connections (attractor model). We moreover demonstrate that the proposed encoding mechanism naturally explains axis alignment of neighbor grid cells and maps shifts, rotations and scaling of the stimuli onto the shape of grid cells' receptive fields, giving a straightforward explanation of the experimental evidence of grid cells remapping under transformations of environmental cues.
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46
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Hasselmo ME, Alexander AS, Dannenberg H, Newman EL. Overview of computational models of hippocampus and related structures: Introduction to the special issue. Hippocampus 2020; 30:295-301. [PMID: 32119171 DOI: 10.1002/hipo.23201] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Extensive computational modeling has focused on the hippocampal formation and associated cortical structures. This overview describes some of the factors that have motivated the strong focus on these structures, including major experimental findings and their impact on computational models. This overview provides a framework for describing the topics addressed by individual articles in this special issue of the journal Hippocampus.
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Affiliation(s)
- Michael E Hasselmo
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts
| | - Andrew S Alexander
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts
| | - Holger Dannenberg
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts
| | - Ehren L Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
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47
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Pastoll H, Garden DL, Papastathopoulos I, Sürmeli G, Nolan MF. Inter- and intra-animal variation in the integrative properties of stellate cells in the medial entorhinal cortex. eLife 2020; 9:52258. [PMID: 32039761 PMCID: PMC7067584 DOI: 10.7554/elife.52258] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 02/04/2020] [Indexed: 01/28/2023] Open
Abstract
Distinctions between cell types underpin organizational principles for nervous system function. Functional variation also exists between neurons of the same type. This is exemplified by correspondence between grid cell spatial scales and the synaptic integrative properties of stellate cells (SCs) in the medial entorhinal cortex. However, we know little about how functional variability is structured either within or between individuals. Using ex-vivo patch-clamp recordings from up to 55 SCs per mouse, we found that integrative properties vary between mice and, in contrast to the modularity of grid cell spatial scales, have a continuous dorsoventral organization. Our results constrain mechanisms for modular grid firing and provide evidence for inter-animal phenotypic variability among neurons of the same type. We suggest that neuron type properties are tuned to circuit-level set points that vary within and between animals.
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Affiliation(s)
- Hugh Pastoll
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Derek L Garden
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Ioannis Papastathopoulos
- The Alan Turing Institute, London, United States.,School of Mathematics, Maxwell Institute and Centre for Statistics, University of Edinburgh, Edinburgh, United Kingdom
| | - Gülşen Sürmeli
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthew F Nolan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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48
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Fukawa A, Aizawa T, Yamakawa H, Eguchi Yairi I. Identifying Core Regions for Path Integration on Medial Entorhinal Cortex of Hippocampal Formation. Brain Sci 2020; 10:brainsci10010028. [PMID: 31948100 PMCID: PMC7016820 DOI: 10.3390/brainsci10010028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 12/31/2019] [Indexed: 12/31/2022] Open
Abstract
Path integration is one of the functions that support the self-localization ability of animals. Path integration outputs position information after an animal’s movement when initial-position and movement information is input. The core region responsible for this function has been identified as the medial entorhinal cortex (MEC), which is part of the hippocampal formation that constitutes the limbic system. However, a more specific core region has not yet been identified. This research aims to clarify the detailed structure at the cell-firing level in the core region responsible for path integration from fragmentarily accumulated experimental and theoretical findings by reviewing 77 papers. This research draws a novel diagram that describes the MEC, the hippocampus, and their surrounding regions by focusing on the MEC’s input/output (I/O) information. The diagram was created by summarizing the results of exhaustively scrutinizing the papers that are relative to the I/O relationship, the connection relationship, and cell position and firing pattern. From additional investigations, we show function information related to path integration, such as I/O information and the relationship between multiple functions. Furthermore, we constructed an algorithmic hypothesis on I/O information and path-integration calculation method from the diagram and the information of functions related to path integration. The algorithmic hypothesis is composed of regions related to path integration, the I/O relations between them, the calculation performed there, and the information representations (cell-firing pattern) in them. Results of examining the hypothesis confirmed that the core region responsible for path integration was either stellate cells in layer II or pyramidal cells in layer III of the MEC.
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Affiliation(s)
- Ayako Fukawa
- Graduate School of Science and Engineering, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan;
- Correspondence: ; Tel.: +81-3-3238-3300
| | - Takahiro Aizawa
- Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;
| | - Hiroshi Yamakawa
- The Whole Brain Architecture Initiative, a Specified Nonprofit Organization, Nishikoiwa 2-19-21, Edogawa-ku, Tokyo 133-0057, Japan;
- Dwango Co., Ltd., KABUKIZA TOWER, 4-12-15 Ginza, Chuo-ku, Tokyo 104-0061, Japan
| | - Ikuko Eguchi Yairi
- Graduate School of Science and Engineering, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan;
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Mok RM, Love BC. A non-spatial account of place and grid cells based on clustering models of concept learning. Nat Commun 2019; 10:5685. [PMID: 31831749 PMCID: PMC6908717 DOI: 10.1038/s41467-019-13760-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 11/24/2019] [Indexed: 11/24/2022] Open
Abstract
One view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so-called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leading to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry.
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Affiliation(s)
- Robert M Mok
- Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.
| | - Bradley C Love
- Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.
- The Alan Turing Institute, London, UK.
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50
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Spalla D, Dubreuil A, Rosay S, Monasson R, Treves A. Can Grid Cell Ensembles Represent Multiple Spaces? Neural Comput 2019; 31:2324-2347. [DOI: 10.1162/neco_a_01237] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The way grid cells represent space in the rodent brain has been a striking discovery, with theoretical implications still unclear. Unlike hippocampal place cells, which are known to encode multiple, environment-dependent spatial maps, grid cells have been widely believed to encode space through a single low-dimensional manifold, in which coactivity relations between different neurons are preserved when the environment is changed. Does it have to be so? Here, we compute, using two alternative mathematical models, the storage capacity of a population of grid-like units, embedded in a continuous attractor neural network, for multiple spatial maps. We show that distinct representations of multiple environments can coexist, as existing models for grid cells have the potential to express several sets of hexagonal grid patterns, challenging the view of a universal grid map. This suggests that a population of grid cells can encode multiple noncongruent metric relationships, a feature that could in principle allow a grid-like code to represent environments with a variety of different geometries and possibly conceptual and cognitive spaces, which may be expected to entail such context-dependent metric relationships.
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Affiliation(s)
| | - Alexis Dubreuil
- Laboratoire de Physique Théorique de l'ENS, 75231 Paris Cedex 05, France
| | - Sophie Rosay
- SISSA, Cognitive Neuroscience, 34136 Trieste, Italy
| | - Remi Monasson
- Laboratoire de Physique Théorique de l'ENS, 75231 Paris Cedex 05, France
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