scholarly journals Neurobiological successor features for spatial navigation

2019 ◽  
Author(s):  
William de Cothi ◽  
Caswell Barry
Keyword(s):  
Grid Cell ◽  
Biological Data ◽  
Basis Set ◽  
Grid Cells ◽  
Dimensional Representation ◽  
Boundary Vector ◽  
Good Account ◽  
Cell Firing ◽  
Low Dimensional ◽  
Environmental Geometry

AbstractThe hippocampus has long been observed to encode a representation of an animal’s position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modelled by learning a successor representation (SR) between distinct positions in an environment. However, this discretisation of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features – boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low-dimensional representation of these successor features. We show that the place and grid cells generated using the BVC-SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.

scholarly journals Can grid cell ensembles represent multiple spaces?

2019 ◽  
Author(s):  
Davide Spalla ◽  
Alexis Dubreuil ◽  
Sophie Rosay ◽  
Remi Monasson ◽  
Alessandro Treves
Keyword(s):  
Storage Capacity ◽  
Grid Cell ◽  
Place Cells ◽  
Dimensional Manifold ◽  
Grid Cells ◽  
Rodent Brain ◽  
Low Dimensional ◽  
Universal Grid ◽  
Low Dimensional Manifold ◽  
Spatial Maps

The way grid cells represent space in the rodent brain has been a striking discovery, with theoret-ical implications still unclear. Differently from 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, em-bedded 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 po-tential 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 non-congruent metric rela-tionships, 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.

scholarly journals Emergent Elasticity in the Neural Code for Space

2018 ◽  
Author(s):  
Samuel Ocko ◽  
Kiah Hardcastle ◽  
Lisa Giocomob ◽  
Surya Ganguli
Keyword(s):  
Neural Circuit ◽  
Topological Defects ◽  
Grid Cell ◽  
Grid Cells ◽  
Motion Cues ◽  
Hebbian Plasticity ◽  
Attractor Model ◽  
Cell Firing ◽  
Path Dependent ◽  
Self Motion

Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including: (1) systematic path-dependent shifts in the firing field of grid cells towards the most recently encountered landmark, even in a fully learned environment, (2) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers, and (3) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.

scholarly journals Emergent elasticity in the neural code for space

2018 ◽  
Vol 115 (50) ◽  
pp. E11798-E11806 ◽  
Author(s):  
Samuel A. Ocko ◽  
Kiah Hardcastle ◽  
Lisa M. Giocomo ◽  
Surya Ganguli
Keyword(s):  
Neural Circuit ◽  
Topological Defects ◽  
Grid Cell ◽  
Grid Cells ◽  
Motion Cues ◽  
Hebbian Plasticity ◽  
Attractor Model ◽  
Cell Firing ◽  
Path Dependent ◽  
Self Motion

Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including (i) systematic path-dependent shifts in the firing fields of grid cells toward the most recently encountered landmark, even in a fully learned environment; (ii) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers; and (iii) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.

scholarly journals Irregular distribution of grid cell firing fields in rats exploring a 3D volumetric space

2021 ◽  
Author(s):  
Roddy M. Grieves ◽  
Selim Jedidi-Ayoub ◽  
Karyna Mishchanchuk ◽  
Anyi Liu ◽  
Sophie Renaudineau ◽  
...  
Keyword(s):  
Grid Cell ◽  
Lower Surface ◽  
Close Packing ◽  
Three Dimensions ◽  
Self Organization ◽  
Grid Cells ◽  
Spatial Regularity ◽  
Cell Firing ◽  
Temporal Firing ◽  
Firing Characteristics

AbstractWe investigated how entorhinal grid cells encode volumetric space. On a horizontal surface, grid cells usually produce multiple, spatially focal, approximately circular firing fields that are evenly sized and spaced to form a regular, close-packed, hexagonal array. This spatial regularity has been suggested to underlie navigational computations. In three dimensions, theoretically the equivalent firing pattern would be a regular, hexagonal close packing of evenly sized spherical fields. In the present study, we report that, in rats foraging in a cubic lattice, grid cells maintained normal temporal firing characteristics and produced spatially stable firing fields. However, although most grid fields were ellipsoid, they were sparser, larger, more variably sized and irregularly arranged, even when only fields abutting the lower surface (equivalent to the floor) were considered. Thus, grid self-organization is shaped by the environment’s structure and/or movement affordances, and grids may not need to be regular to support spatial computations.

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 Deforming the metric of cognitive maps distorts memory

2018 ◽  
Author(s):  
Jacob L. S. Bellmund ◽  
William de Cothi ◽  
Tom A. Ruiter ◽  
Matthias Nau ◽  
Caswell Barry ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Cell Model ◽  
Cell Activity ◽  
Grid Cell ◽  
Cognitive Maps ◽  
Grid Cells ◽  
Boundary Geometry ◽  
Frequency Changes ◽  
Environmental Geometry ◽  
The Impact

AbstractEnvironmental boundaries anchor cognitive maps that support memory. However, trapezoidal boundary geometry distorts the regular firing patterns of entorhinal grid cells proposedly providing a metric for cognitive maps. Here, we test the impact of trapezoidal boundary geometry on human spatial memory using immersive virtual reality. Consistent with reduced regularity of grid patterns in rodents and a grid-cell model based on the eigenvectors of the successor representation, human positional memory was degraded in a trapezoid compared to a square environment; an effect particularly pronounced in the trapezoid’s narrow part. Congruent with spatial frequency changes of eigenvector grid patterns, distance estimates between remembered positions were persistently biased; revealing distorted memory maps that explained behavior better than the objective maps. Our findings demonstrate that environmental geometry affects human spatial memory similarly to rodent grid cell activity — thus strengthening the putative link between grid cells and behavior along with their cognitive functions beyond navigation.

scholarly journals A computational model that explores the effect of environmental geometries on grid cell representations

2017 ◽  
Author(s):  
Samyukta Jayakumar ◽  
Rukhmani Narayanamurthy ◽  
Reshma Ramesh ◽  
Karthik Soman ◽  
Vignesh Muralidharan ◽  
...  
Keyword(s):  
Cell Activity ◽  
Grid Cell ◽  
Point Of View ◽  
Experimental Results ◽  
Grid Cells ◽  
Computational Point ◽  
Model Code ◽  
Wide Range ◽  
Environmental Geometry ◽  
Geometry Analysis

AbstractGrid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern was originally considered to be invariant to the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al 2015) and in connected environments (Carpenter et al 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the grid cell periodicity from a computational point of view using a model that was successful in generating a wide range of spatial cells, including grid cells, place cells, head direction cells and border cells. We simulated the model in four types of environmental geometries such as: 1) connected environments, 2) convex shapes, 3) concave shapes and 4) regular polygons with varying number of sides. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model code not just for local position but also for more global information like the shape of the environment. The proposed model is interesting not only because it was able to capture the aforementioned experimental results but, more importantly, it was able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity.

scholarly journals Altered neural odometry in the vertical dimension

2019 ◽  
Vol 116 (10) ◽  
pp. 4631-4636 ◽  
Author(s):  
Giulio Casali ◽  
Daniel Bush ◽  
Kate Jeffery
Keyword(s):  
Vertical Plane ◽  
Grid Cell ◽  
Vertical Surface ◽  
The Body ◽  
Grid Cells ◽  
Grid Pattern ◽  
3D Space ◽  
Cell Firing ◽  
Circular Grid ◽  
Self Motion

Entorhinal grid cells integrate sensory and self-motion inputs to provide a spatial metric of a characteristic scale. One function of this metric may be to help localize the firing fields of hippocampal place cells during formation and use of the hippocampal spatial representation (“cognitive map”). Of theoretical importance is the question of how this metric, and the resulting map, is configured in 3D space. We find here that when the body plane is vertical as rats climb a wall, grid cells produce stable, almost-circular grid-cell firing fields. This contrasts with previous findings when the body was aligned horizontally during vertical exploration, suggesting a role for the body plane in orienting the plane of the grid cell map. However, in the present experiment, the fields on the wall were fewer and larger, suggesting an altered or absent odometric (distance-measuring) process. Several physiological indices of running speed in the entorhinal cortex showed reduced gain, which may explain the enlarged grid pattern. Hippocampal place fields were found to be sparser but unchanged in size/shape. Together, these observations suggest that the orientation and scale of the grid cell map, at least on a surface, are determined by an interaction between egocentric information (the body plane) and allocentric information (the gravity axis). This may be mediated by the different sensory or locomotor information available on a vertical surface and means that the resulting map has different properties on a vertical plane than a horizontal plane (i.e., is anisotropic).

scholarly journals Can Grid Cell Ensembles Represent Multiple Spaces?

2019 ◽  
Vol 31 (12) ◽  
pp. 2324-2347 ◽  
Author(s):  
Davide Spalla ◽  
Alexis Dubreuil ◽  
Sophie Rosay ◽  
Remi Monasson ◽  
Alessandro Treves
Keyword(s):  
Storage Capacity ◽  
Grid Cell ◽  
Place Cells ◽  
Dimensional Manifold ◽  
Grid Cells ◽  
Rodent Brain ◽  
Low Dimensional ◽  
Universal Grid ◽  
Low Dimensional Manifold ◽  
Spatial Maps

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.

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.

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