scholarly journals Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations

2014 ◽  
Vol 369 (1635) ◽  
pp. 20120524 ◽  
Author(s):  
Stephen Grossberg ◽  
Praveen K. Pilly
Keyword(s):  
Entorhinal Cortex ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Grid Cell ◽  
Neural Model ◽  
Gamma Oscillations ◽  
Place Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Map Learning

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC (‘neural relativity’). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

Learning Maps to Navigate Space

2021 ◽  
pp. 572-617
Author(s):  
Stephen Grossberg
Keyword(s):  
Entorhinal Cortex ◽  
Spatial Scales ◽  
Grid Cell ◽  
Gamma Oscillations ◽  
Place Cells ◽  
Self Organizing Map ◽  
Grid Cells ◽  
Map Learning ◽  
Neurophysiological Data ◽  
Self Organizing

This chapter explains how humans and other animals learn to learn to navigate in space. Both reaching and route-based navigation use difference vector computations. Route navigation learns a labeled graph of angles and distances moved. Spatial navigation requires neurons to learn navigable spaces that can be many meters in size. This is again accomplished by a spectrum of cells. Such spectral spacing supports learning of medial entorhinal grid cells and hippocampal place cells. The model responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells develop in a hierarchy of self-organizing maps by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. Model parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same self-organizing map mechanisms can learn both grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple grid cell modules through medial entorhinal cortex to hippocampus uses a gradient of rates that is homologous to a rate gradient that drives adaptively timed learning at multiple rates through lateral entorhinal cortex to hippocampus (‘neural relativity’). The model clarifies how top-down hippocampal-to-entorhinal ART attentional mechanisms stabilize map learning, simulates how hippocampal, septal, or acetylcholine inactivation disrupts grid cells, and explains data about theta, beta and gamma oscillations.

scholarly journals 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 ◽  
Vol 9 ◽  
Author(s):  
Haggai Agmon ◽  
Yoram Burak
Keyword(s):  
Entorhinal Cortex ◽  
Activity Patterns ◽  
Grid Cell ◽  
Mechanistic Explanation ◽  
Place Cells ◽  
Mammalian Brain ◽  
Dimensional Manifold ◽  
Grid Cells ◽  
Attractor Dynamics ◽  
Low Dimensional

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.

How Do Spatial Learning and Memory Occur in the Brain? Coordinated Learning of Entorhinal Grid Cells and Hippocampal Place Cells

2012 ◽  
Vol 24 (5) ◽  
pp. 1031-1054 ◽  
Author(s):  
Praveen K. Pilly ◽  
Stephen Grossberg
Keyword(s):  
Learning And Memory ◽  
Entorhinal Cortex ◽  
Spatial Learning ◽  
Spatial Scales ◽  
Place Cells ◽  
Spatial Learning And Memory ◽  
Grid Cells ◽  
Self Organizing Maps ◽  
Neurophysiological Data ◽  
Self Organizing

Spatial learning and memory are important for navigation and formation of episodic memories. The hippocampus and medial entorhinal cortex (MEC) are key brain areas for spatial learning and memory. Place cells in hippocampus fire whenever an animal is located in a specific region in the environment. Grid cells in the superficial layers of MEC provide inputs to place cells and exhibit remarkable regular hexagonal spatial firing patterns. They also exhibit a gradient of spatial scales along the dorsoventral axis of the MEC, with neighboring cells at a given dorsoventral location having different spatial phases. A neural model shows how a hierarchy of self-organizing maps, each obeying the same laws, responds to realistic rat trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with unimodal firing fields that fit neurophysiological data about their development in juvenile rats. The hippocampal place fields represent much larger spaces than the grid cells to support navigational behaviors. Both the entorhinal and hippocampal self-organizing maps amplify and learn to categorize the most energetic and frequent co-occurrences of their inputs. Top–down attentional mechanisms from hippocampus to MEC help to dynamically stabilize these spatial memories in both the model and neurophysiological data. Spatial learning through MEC to hippocampus occurs in parallel with temporal learning through lateral entorhinal cortex to hippocampus. These homologous spatial and temporal representations illustrate a kind of “neural relativity” that may provide a substrate for episodic learning and memory.

scholarly journals A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial hippocampal remapping and individual grid cell field-to-field variability

2020 ◽  
Author(s):  
Haggai Agmon ◽  
Yoram Burak
Keyword(s):  
Entorhinal Cortex ◽  
Activity Patterns ◽  
Grid Cell ◽  
Mechanistic Explanation ◽  
Place Cells ◽  
Dimensional Manifold ◽  
Grid Cells ◽  
Attractor Dynamics ◽  
Low Dimensional ◽  
Low Dimensional Manifold

ABSTRACTThe representation of position in the 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. We then show that 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.

scholarly journals Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Yedidyah Dordek ◽  
Daniel Soudry ◽  
Ron Meir ◽  
Dori Derdikman
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Hexagonal Lattice ◽  
Grid Cell ◽  
Principal Component ◽  
Component Analysis ◽  
Place Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Spacing Ratio

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.

scholarly journals Disrupted remapping of place cells and grid cells in knock-in model of Alzheimer’s disease

2019 ◽  
Author(s):  
Heechul Jun ◽  
Shogo Soma ◽  
Takashi Saito ◽  
Takaomi C Saido ◽  
Kei M Igarashi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Memory ◽  
Memory Impairment ◽  
Grid Cell ◽  
Gamma Oscillations ◽  
Place Cells ◽  
Grid Cells ◽  
Model Of Alzheimer’S Disease ◽  
Circuit Function

AbstractPatients with Alzheimer’s disease (AD) frequently suffer from spatial memory impairment and wandering behavior, but brain circuits causing such symptoms remain largely unclear. In healthy brains, spatially-tuned hippocampal place cells and entorhinal grid cells represent distinct spike patterns in different environments, a circuit function called “remapping” that underlies pattern separation of spatial memory. We investigated whether knock-in expression of mutated amyloid precursor protein deteriorates the remapping of place cells and grid cells. We found that the remapping of CA1 place cells was disrupted although their spatial tuning was only mildly diminished. Grid cells in the medial entorhinal cortex (MEC) were impaired, sending severely disrupted remapping signals to the hippocampus. Furthermore, fast gamma oscillations were disrupted in both CA1 and MEC, resulting in impaired fast gamma coupling in the MEC→CA1 circuit. These results point to the link between grid cell impairment and remapping disruption as a circuit mechanism causing spatial memory impairment in AD.

scholarly journals Path integration in place cells of developing rats

2018 ◽  
Vol 115 (7) ◽  
pp. E1637-E1646 ◽  
Author(s):  
Tale L. Bjerknes ◽  
Nenitha C. Dagslott ◽  
Edvard I. Moser ◽  
May-Britt Moser
Keyword(s):  
Path Integration ◽  
Grid Cell ◽  
Cell System ◽  
Place Cells ◽  
Postnatal Life ◽  
Motion Information ◽  
Grid Cells ◽  
Adult Rats ◽  
Self Motion ◽  
Made In

Place cells in the hippocampus and grid cells in the medial entorhinal cortex rely on self-motion information and path integration for spatially confined firing. Place cells can be observed in young rats as soon as they leave their nest at around 2.5 wk of postnatal life. In contrast, the regularly spaced firing of grid cells develops only after weaning, during the fourth week. In the present study, we sought to determine whether place cells are able to integrate self-motion information before maturation of the grid-cell system. Place cells were recorded on a 200-cm linear track while preweaning, postweaning, and adult rats ran on successive trials from a start wall to a box at the end of a linear track. The position of the start wall was altered in the middle of the trial sequence. When recordings were made in complete darkness, place cells maintained fields at a fixed distance from the start wall regardless of the age of the animal. When lights were on, place fields were determined primarily by external landmarks, except at the very beginning of the track. This shift was observed in both young and adult animals. The results suggest that preweaning rats are able to calculate distances based on information from self-motion before the grid-cell system has matured to its full extent.

Grid Cells Lose Coherence in Realistic Environments

2021 ◽  
Author(s):  
Yifan Luo ◽  
Matteo Toso ◽  
Bailu Si ◽  
Federico Stella ◽  
Alessandro Treves
Keyword(s):  
Short Range ◽  
Short Range Order ◽  
Grid Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Lateral Connectivity ◽  
Cell Pattern ◽  
In The Wild ◽  
Freely Moving ◽  
Adaptation Model

Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an incoherent motley of unit clusters, flexible or outright unstable.

scholarly journals Functional connectivity of the entorhinal–hippocampal space circuit

2014 ◽  
Vol 369 (1635) ◽  
pp. 20120516 ◽  
Author(s):  
Sheng-Jia Zhang ◽  
Jing Ye ◽  
Jonathan J. Couey ◽  
Menno Witter ◽  
Edvard I. Moser ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Cell Types ◽  
Place Cells ◽  
Projection Neurons ◽  
Border Cells ◽  
Head Direction ◽  
Grid Cells ◽  
Head Direction Cells ◽  
Dynamic Interactions ◽  
Cell Groups

The mammalian space circuit is known to contain several functionally specialized cell types, such as place cells in the hippocampus and grid cells, head-direction cells and border cells in the medial entorhinal cortex (MEC). The interaction between the entorhinal and hippocampal spatial representations is poorly understood, however. We have developed an optogenetic strategy to identify functionally defined cell types in the MEC that project directly to the hippocampus. By expressing channelrhodopsin-2 (ChR2) selectively in the hippocampus-projecting subset of entorhinal projection neurons, we were able to use light-evoked discharge as an instrument to determine whether specific entorhinal cell groups—such as grid cells, border cells and head-direction cells—have direct hippocampal projections. Photoinduced firing was observed at fixed minimal latencies in all functional cell categories, with grid cells as the most abundant hippocampus-projecting spatial cell type. We discuss how photoexcitation experiments can be used to distinguish the subset of hippocampus-projecting entorhinal neurons from neurons that are activated indirectly through the network. The functional breadth of entorhinal input implied by this analysis opens up the potential for rich dynamic interactions between place cells in the hippocampus and different functional cell types in the entorhinal cortex (EC).

scholarly journals Optimal Population Codes for Space: Grid Cells Outperform Place Cells

2012 ◽  
Vol 24 (9) ◽  
pp. 2280-2317 ◽  
Author(s):  
Alexander Mathis ◽  
Andreas V. M. Herz ◽  
Martin Stemmler
Keyword(s):  
Spatial Resolution ◽  
A Priori ◽  
Hexagonal Lattice ◽  
Grid Cell ◽  
Likelihood Estimation ◽  
Spatial Location ◽  
Neuronal Population ◽  
Place Cells ◽  
Grid Cells ◽  
Tuning Curves

Rodents use two distinct neuronal coordinate systems to estimate their position: place fields in the hippocampus and grid fields in the entorhinal cortex. Whereas place cells spike at only one particular spatial location, grid cells fire at multiple sites that correspond to the points of an imaginary hexagonal lattice. We study how to best construct place and grid codes, taking the probabilistic nature of neural spiking into account. Which spatial encoding properties of individual neurons confer the highest resolution when decoding the animal's position from the neuronal population response? A priori, estimating a spatial position from a grid code could be ambiguous, as regular periodic lattices possess translational symmetry. The solution to this problem requires lattices for grid cells with different spacings; the spatial resolution crucially depends on choosing the right ratios of these spacings across the population. We compute the expected error in estimating the position in both the asymptotic limit, using Fisher information, and for low spike counts, using maximum likelihood estimation. Achieving high spatial resolution and covering a large range of space in a grid code leads to a trade-off: the best grid code for spatial resolution is built of nested modules with different spatial periods, one inside the other, whereas maximizing the spatial range requires distinct spatial periods that are pairwisely incommensurate. Optimizing the spatial resolution predicts two grid cell properties that have been experimentally observed. First, short lattice spacings should outnumber long lattice spacings. Second, the grid code should be self-similar across different lattice spacings, so that the grid field always covers a fixed fraction of the lattice period. If these conditions are satisfied and the spatial “tuning curves” for each neuron span the same range of firing rates, then the resolution of the grid code easily exceeds that of the best possible place code with the same number of neurons.

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