scholarly journals Effect of reward on electrophysiological signatures of grid cell population activity in human spatial navigation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenjing Wang ◽  
Wenxu Wang
Keyword(s):  
Cell Population ◽  
Entorhinal Cortex ◽  
Grid Cell ◽  
Grid Cells ◽  
Grid Pattern ◽  
Population Activity ◽  
Spatial Measures ◽  
Moving Direction ◽  
Patients With Epilepsy ◽  
Reward Information

AbstractThe regular equilateral triangular periodic firing pattern of grid cells in the entorhinal cortex is considered a regular metric for the spatial world, and the grid-like representation correlates with hexadirectional modulation of theta (4–8 Hz) power in the entorhinal cortex relative to the moving direction. However, researchers have not clearly determined whether grid cells provide only simple spatial measures in human behavior-related navigation strategies or include other factors such as goal rewards to encode information in multiple patterns. By analysing the hexadirectional modulation of EEG signals in the theta band in the entorhinal cortex of patients with epilepsy performing spatial target navigation tasks, we found that this modulation presents a grid pattern that carries target-related reward information. This grid-like representation is influenced by explicit goals and is related to the local characteristics of the environment. This study provides evidence that human grid cell population activity is influenced by reward information at the level of neural oscillations.

scholarly journals Toroidal topology of population activity in grid cells

Nature ◽  
2022 ◽  
Author(s):  
Richard J. Gardner ◽  
Erik Hermansen ◽  
Marius Pachitariu ◽  
Yoram Burak ◽  
Nils A. Baas ◽  
...  
Keyword(s):  
Grid Cell ◽  
Population Level ◽  
Neural System ◽  
Topological Data Analysis ◽  
Small Samples ◽  
Grid Cells ◽  
Population Code ◽  
Grid Pattern ◽  
Population Activity ◽  
Toroidal Manifold

AbstractThe 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.

scholarly journals Toroidal topology of population activity in grid cells

2021 ◽  
Author(s):  
Richard J. Gardner ◽  
Erik Hermansen ◽  
Marius Pachitariu ◽  
Yoram Burak ◽  
Nils A. Baas ◽  
...  
Keyword(s):  
Grid Cell ◽  
Population Level ◽  
Neural System ◽  
Topological Data Analysis ◽  
Small Samples ◽  
Grid Cells ◽  
Population Code ◽  
Grid Pattern ◽  
Population Activity ◽  
Toroidal Manifold

AbstractThe medial entorhinal cortex (MEC) is part of a neural system for mapping a subject’s position within a physical environment1,2. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations3, and are organized in modules4,5 which collectively form a population code for the animal’s allocentric position1,6–8. The invariance of the correlation structure of this population code across environments9,10 and behavioural states11,12, independently of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,2,13–16. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained elusive due to the small samples of cells obtained to date. Here we show, with simultaneous recordings from many hundreds of grid cells, and subsequent topological data analysis, 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 the moving animal’s position in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained, with minimal distortion, between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feed-forward models where grid patterns are created from external inputs by Hebbian plasticity17–22. This demonstration of network dynamics on a toroidal manifold provides the first population-level visualization of CAN dynamics in grid cells.

scholarly journals Robust and Efficient Coding with Grid Cells

2017 ◽  
Author(s):  
Lajos Vágó ◽  
Balázs B Ujfalussy
Keyword(s):  
Entorhinal Cortex ◽  
Upper Bound ◽  
Grid Cell ◽  
Parameter Tuning ◽  
Grid Cells ◽  
Theoretic Analysis ◽  
Population Activity ◽  
Efficient Coding ◽  
Neuronal Code ◽  
Random Scale

AbstractThe neuronal code arising from the coordinated population activity of grid cells in the rodent entorhinal cortex can uniquely represent space across large distances but the precise conditions for efficient coding are unknown. Here we present a number-theoretic analysis of grid coding and derive an upper bound on the distance that a population of grid cells can represent without error. We show that in the absence of neuronal noise, the capacity of the system would be extremely sensitive to the choice of the grid periods. However, when the accuracy of the representation is limited by neuronal noise, the capacity becomes gradually more robust against the choice of grid scales as the number of modules increases and remains near optimal even for random scale choices. Our study reveals that robust and efficient coding can be achieved without parameter tuning in the case of grid cell representation.

scholarly journals An isomorphic mapping hypothesis of the grid representation

2014 ◽  
Vol 369 (1635) ◽  
pp. 20120521 ◽  
Author(s):  
Michael Brecht ◽  
Saikat Ray ◽  
Andrea Burgalossi ◽  
Qiusong Tang ◽  
Helene Schmidt ◽  
...  
Keyword(s):  
Pyramidal Neurons ◽  
Dimensional Space ◽  
Grid Cell ◽  
Propagation Speed ◽  
Connectivity Matrix ◽  
Two Dimensional ◽  
Grid Cells ◽  
Population Activity ◽  
Medial Entorhinal Cortex ◽  
Mapping Hypothesis

We introduce a grid cell microcircuit hypothesis. We propose the ‘grid in the world’ (evident in grid cell discharges) is generated by a ‘grid in the cortex’. This cortical grid is formed by patches of calbindin-positive pyramidal neurons in layer 2 of medial entorhinal cortex (MEC). Our isomorphic mapping hypothesis assumes three types of isomorphism: (i) metric correspondence of neural space (the two-dimensional cortical sheet) and the external two-dimensional space within patches; (ii) isomorphism between cellular connectivity matrix and firing field; (iii) isomorphism between single cell and population activity. Each patch is a grid cell lattice arranged in a two-dimensional map of space with a neural : external scale of approximately 1 : 2000 in the dorsal part of rat MEC. The lattice behaves like an excitable medium with neighbouring grid cells exciting each other. Spatial scale is implemented as an intrinsic scaling factor for neural propagation speed. This factor varies along the dorsoventral cortical axis. A connectivity scheme of the grid system is described. Head direction input specifies the direction of activity propagation. We extend the theory to neurons between grid patches and predict a rare discharge pattern (inverted grid cells) and the relative location and proportion of grid cells and spatial band cells.

scholarly journals Home, head direction stability, and grid cell distortion

2020 ◽  
Vol 123 (4) ◽  
pp. 1392-1406 ◽  
Author(s):  
Juan Ignacio Sanguinetti-Scheck ◽  
Michael Brecht
Keyword(s):  
Entorhinal Cortex ◽  
Home Cage ◽  
Cell Activity ◽  
Grid Cell ◽  
Head Direction ◽  
Grid Cells ◽  
Head Direction Cells ◽  
Medial Entorhinal Cortex ◽  
Abstract Approach ◽  
Globally Stable

The home is a unique location in the life of humans and animals. In rats, home presents itself as a multicompartmental space that involves integrating navigation through subspaces. Here we embedded the laboratory rat’s home cage in the arena, while recording neurons in the animal’s parasubiculum and medial entorhinal cortex, two brain areas encoding the animal’s location and head direction. We found that head direction signals were unaffected by home cage presence or translocation. Head direction cells remain globally stable and have similar properties inside and outside the embedded home. We did not observe egocentric bearing encoding of the home cage. However, grid cells were distorted in the presence of the home cage. While they did not globally remap, single firing fields were translocated toward the home. These effects appeared to be geometrical in nature rather than a home-specific distortion and were not dependent on explicit behavioral use of the home cage during a hoarding task. Our work suggests that medial entorhinal cortex and parasubiculum do not remap after embedding the home, but local changes in grid cell activity overrepresent the embedded space location and might contribute to navigation in complex environments. NEW & NOTEWORTHY Neural findings in the field of spatial navigation come mostly from an abstract approach that separates the animal from even a minimally biological context. In this article we embed the home cage of the rat in the environment to address some of the complexities of natural navigation. We find no explicit home cage representation. While both head direction cells and grid cells remain globally stable, we find that embedded spaces locally distort grid cells.

scholarly journals Entorhinal cortex receptive fields are modulated by spatial attention, even without movement

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Niklas Wilming ◽  
Peter König ◽  
Seth König ◽  
Elizabeth A Buffalo
Keyword(s):  
Entorhinal Cortex ◽  
Cell Activity ◽  
Receptive Fields ◽  
Grid Cell ◽  
Covert Attention ◽  
Grid Cells ◽  
Physical Movement ◽  
Mental Operations ◽  
Triangular Tiling ◽  
Central Fixation

Grid cells in the entorhinal cortex allow for the precise decoding of position in space. Along with potentially playing an important role in navigation, grid cells have recently been hypothesized to make a general contribution to mental operations. A prerequisite for this hypothesis is that grid cell activity does not critically depend on physical movement. Here, we show that movement of covert attention, without any physical movement, also elicits spatial receptive fields with a triangular tiling of space. In monkeys trained to maintain central fixation while covertly attending to a stimulus moving in the periphery we identified a significant population (20/141, 14% neurons at a FDR <5%) of entorhinal cells with spatially structured receptive fields. This contrasts with recordings obtained in the hippocampus, where grid-like representations were not observed. Our results provide evidence that neurons in macaque entorhinal cortex do not rely on physical movement.

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.

scholarly journals Home, head direction stability and grid cell distortion

2019 ◽  
Author(s):  
Juan Ignacio Sanguinetti-Scheck ◽  
Michael Brecht
Keyword(s):  
Entorhinal Cortex ◽  
Home Cage ◽  
Grid Cell ◽  
Neural Representation ◽  
Head Direction ◽  
Grid Cells ◽  
Medial Entorhinal Cortex ◽  
Home Location ◽  
Single Firing ◽  
Behavioral Studies

AbstractThe home is a unique location in the life of humans and animals. Numerous behavioral studies investigating homing indicate that many animals maintain an online representation of the direction of the home, a home vector. Here we placed the rat’s home cage in the arena, while recording neurons in the animal’s parasubiculum and medial entorhinal cortex. From a pellet hoarding paradigm it became evident that the home cage induced locomotion patterns characteristic of homing behaviors. We did not observe home-vector cells. We found that head-direction signals were unaffected by home location. However, grid cells were distorted in the presence of the home cage. While they did not globally remap, single firing fields were translocated towards the home. These effects appeared to be geometrical in nature rather than a home-specific distortion. Our work suggests that medial entorhinal cortex and parasubiculum do not contain an explicit neural representation of the home direction.

scholarly journals Connecting multiple spatial scales to decode the population activity of grid cells

2015 ◽  
Vol 1 (11) ◽  
pp. e1500816 ◽  
Author(s):  
Martin Stemmler ◽  
Alexander Mathis ◽  
Andreas V. M. Herz
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Cell Activity ◽  
Dead Reckoning ◽  
Grid Cell ◽  
Grid Cells ◽  
Population Vector ◽  
Population Activity ◽  
Multiple Spatial Scales ◽  
Scale Ratio

Mammalian grid cells fire when an animal crosses the points of an imaginary hexagonal grid tessellating the environment. We show how animals can navigate by reading out a simple population vector of grid cell activity across multiple spatial scales, even though neural activity is intrinsically stochastic. This theory of dead reckoning explains why grid cells are organized into discrete modules within which all cells have the same lattice scale and orientation. The lattice scale changes from module to module and should form a geometric progression with a scale ratio of around 3/2 to minimize the risk of making large-scale errors in spatial localization. Such errors should also occur if intermediate-scale modules are silenced, whereas knocking out the module at the smallest scale will only affect spatial precision. For goal-directed navigation, the allocentric grid cell representation can be readily transformed into the egocentric goal coordinates needed for planning movements. The goal location is set by nonlinear gain fields that act on goal vector cells. This theory predicts neural and behavioral correlates of grid cell readout that transcend the known link between grid cells of the medial entorhinal cortex and place cells of the hippocampus.

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.

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