1
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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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2
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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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3
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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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4
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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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5
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Yu N, Liao Y, Yu H, Sie O. Construction of the rat brain spatial cell firing model on a quadruped robot. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Naigong Yu
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
| | - Yishen Liao
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
| | - Hejie Yu
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
| | - Ouattara Sie
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
- College of Robotic Université Félix Houphouët‐Boigny Abidjan Côte d'Ivoire
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6
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Yu N, Yu H, Liao Y, Wang Z, Sie O. A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5607999. [PMID: 34745501 PMCID: PMC8564186 DOI: 10.1155/2021/5607999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/26/2021] [Accepted: 10/12/2021] [Indexed: 12/03/2022]
Abstract
Physiological studies have shown that the hippocampal structure of rats develops at different stages, in which the place cells continue to develop during the whole juvenile period of rats and mature after the juvenile period. As the main information source of place cells, grid cells should mature earlier than place cells. In order to make better use of the biological information exhibited by the rat brain hippocampus in the environment, we propose a position cognition model based on the spatial cell development mechanism of rat hippocampus. The model uses a recurrent neural network with parametric bias (RNNPB) to simulate changes in the discharge characteristics during the development of a single stripe cell. The oscillatory interference mechanism is able to fuse the developing stripe waves, thus indirectly simulating the developmental process of the grid cells. The output of the grid cells is then used as the information input of the place cells, whose development process is simulated by BP neural network. After the place cells matured, the position matrix generated by the place cell group was used to realize the position cognition of rats in a given spatial region. The experimental results show that this model can simulate the development process of grid cells and place cells, and it can realize high precision positioning in the given space area. Moreover, the experimental effect of cognitive map construction using this model is basically consistent with the effect of RatSLAM, which verifies the validity and accuracy of the model.
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Affiliation(s)
- Naigong Yu
- Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
| | - Hejie Yu
- Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
| | - Yishen Liao
- Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
| | - Zongxia Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
| | - Ouattara Sie
- Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
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7
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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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Tukker JJ, Beed P, Brecht M, Kempter R, Moser EI, Schmitz D. Microcircuits for spatial coding in the medial entorhinal cortex. Physiol Rev 2021; 102:653-688. [PMID: 34254836 PMCID: PMC8759973 DOI: 10.1152/physrev.00042.2020] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [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
- Network Dysfunction, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Prateep Beed
- NeuroScientific Research Center, Charite Berlin, Germany
| | - Michael Brecht
- Systems Neuroscience, Humboldt University of Berlin, Berlin, Germany
| | - Richard Kempter
- Department of Biology, Institute for Theoretical Biology, Humbolt-Universität zu Berlin, Berlin, Germany
| | - Edvard I Moser
- Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology
| | - Dietmar Schmitz
- Neuroscience Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
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9
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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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10
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A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:1492429. [PMID: 32849862 PMCID: PMC7439180 DOI: 10.1155/2020/1492429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 04/24/2020] [Indexed: 11/17/2022]
Abstract
Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.
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11
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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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12
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Park SW, Jang HJ, Kim M, Kwag J. Spatiotemporally random and diverse grid cell spike patterns contribute to the transformation of grid cell to place cell in a neural network model. PLoS One 2019; 14:e0225100. [PMID: 31725775 PMCID: PMC6855461 DOI: 10.1371/journal.pone.0225100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 10/29/2019] [Indexed: 12/13/2022] Open
Abstract
The medial entorhinal cortex and the hippocampus are brain regions specialized in spatial information processing. While an animal navigates around an environment, grid cells in the medial entorhinal cortex spike at multiple discrete locations, forming hexagonal grid patterns, and each grid cell is spatiotemporally dynamic with a different grid size, spacing, and orientation. In contrast, place cells in the hippocampus spike when an animal is at one or more specific locations, called a “place field”. While an animal traverses through a place field, the place cell’s spike phases relative to the hippocampal theta-frequency oscillation advance in phase, known as the “spike phase precession” phenomenon and each spike encodes the specific location within the place field. Interestingly, the medial entorhinal cortical grid cells and the hippocampal place cells are only one excitatory synapse apart. However, how the spatiotemporally dynamic multi-peaked grid cell activities are transformed into hippocampal place cell activities with spike phase precession phenomenon is yet unknown. To address this question, we construct an anatomically and physiologically realistic neural network model comprised of 10,000 grid cell models, each with a spatiotemporally dynamic grid patterns and a place cell model connected by excitatory synapses. Using this neural network model, we show that grid cells’ spike activities with spatiotemporally random and diverse grid orientation, spacing, and phases as inputs to place cell are able to generate a place field with spike phase precession. These results indicate that spatiotemporally random and diverse grid cell spike activities are essential for the formation of place cell activity observed in vivo.
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Affiliation(s)
- Sahn Woo Park
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Hyun Jae Jang
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Mincheol Kim
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Jeehyun Kwag
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- * E-mail:
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13
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Weber SN, Sprekeler H. Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity. eLife 2018; 7:34560. [PMID: 29465399 PMCID: PMC5927772 DOI: 10.7554/elife.34560] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 02/19/2018] [Indexed: 01/27/2023] Open
Abstract
Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns - in both their selectivity and their invariance - arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions.
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Affiliation(s)
- Simon Nikolaus Weber
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer ScienceTechnische Universität BerlinBerlinGermany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer ScienceTechnische Universität BerlinBerlinGermany
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14
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D’Albis T, Kempter R. A single-cell spiking model for the origin of grid-cell patterns. PLoS Comput Biol 2017; 13:e1005782. [PMID: 28968386 PMCID: PMC5638623 DOI: 10.1371/journal.pcbi.1005782] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 10/12/2017] [Accepted: 09/18/2017] [Indexed: 11/19/2022] Open
Abstract
Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex, yet the fundamental principles underlying the origin of grid-cell firing are still debated. Grid-like patterns could emerge via Hebbian learning and neuronal adaptation, but current computational models remained too abstract to allow direct confrontation with experimental data. Here, we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity. Through rigorous mathematical analysis applicable in the linear limit, we quantitatively predict the requirements for grid-pattern formation, and we establish a direct link to classical pattern-forming systems of the Turing type. Our study lays the groundwork for biophysically-realistic models of grid-cell activity.
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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, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
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15
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Dordek Y, Soudry D, Meir R, Derdikman D. Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis. eLife 2016; 5:e10094. [PMID: 26952211 PMCID: PMC4841785 DOI: 10.7554/elife.10094] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 03/08/2016] [Indexed: 11/13/2022] Open
Abstract
Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is -1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA.
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Affiliation(s)
- Yedidyah Dordek
- Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.,Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Daniel Soudry
- Department of Statistics, Columbia University, New York, United States.,Center for Theoretical Neuroscience, Columbia University, New York, United States
| | - Ron Meir
- Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Dori Derdikman
- Rappaport Faculty of Medicine and Research Institute, Technion - Israel Institute of Technology, Haifa, Israel
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Glenberg AM, Hayes J. Contribution of Embodiment to Solving the Riddle of Infantile Amnesia. Front Psychol 2016; 7:10. [PMID: 26834683 PMCID: PMC4724724 DOI: 10.3389/fpsyg.2016.00010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Accepted: 01/05/2016] [Indexed: 11/17/2022] Open
Abstract
At least since the late nineteenth century, researchers have sought an explanation for infantile amnesia (IA)-the lack of autobiographical memories dating from early childhood-and childhood amnesia (CA), faster forgetting of events up until the age of about seven. Evidence suggests that IA occurs across altricial species, and a number of studies using animal models have converged on the hypothesis that maturation of the hippocampus is an important factor. But why does the hippocampus mature at one time and not another, and how does that maturation relate to memory? Our hypothesis is rooted in theories of embodied cognition, and it provides an explanation both for hippocampal development and the end of IA. Specifically, the onset of locomotion prompts the alignment of hippocampal place cells and grid cells to the environment, which in turn facilitates the ontogeny of long-term episodic memory and the end of IA. That is, because the animal can now reliably discriminate locations, location becomes a stable cue for memories. Furthermore, as the mode of human locomotion shifts from crawling to walking, there is an additional shift in the alignment of the hippocampus that marks the beginning of adult-like episodic memory and the end of CA. Finally, given a reduction in self-locomotion and exploration with aging, the hypothesis suggests a partial explanation for cognitive decline with aging.
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Affiliation(s)
- Arthur M. Glenberg
- Department of Psychology, Arizona State University, TempeAZ, USA
- Department of Psychology, University of Wisconsin-Madison, MadisonWI, USA
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