scholarly journals Grid pattern development, distortions and topological defects may depend on distributed anchoring

2019 ◽  
Author(s):  
Maria Mørreaunet ◽  
Martin Hägglund
Keyword(s):  
Network Model ◽  
Firing Pattern ◽  
Topological Defects ◽  
Place Cells ◽  
Local Network ◽  
Grid Cells ◽  
Grid Pattern ◽  
Pattern Development ◽  
Self Motion ◽  
Network Attractor

AbstractThe firing pattern of grid cells in rats has been shown to exhibit elastic distortions that compresses and shears the pattern and suggests that the grid is locally anchored. Anchoring points may need to be learned to account for different environments. We recorded grid cells in animals encountering a novel environment. The grid pattern was not stable but moved between the first few sessions predicted by the animals running behavior. Using a learning continuous attractor network model, we show that learning distributed anchoring points may lead to such grid field movement as well as previously observed shearing and compression distortions. The model further predicted topological defects comprising a pentagonal/heptagonal break in the pattern. Grids recorded in large environments were shown to exhibit such topological defects. Taken together, the final pattern may be a compromise between local network attractor states driven by self-motion signals and distributed anchoring inputs from place cells.

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.

scholarly journals Modeling place cells and grid cells in multi-compartment environments: hippocampal-entorhinal loop as a multisensory integration circuit

2019 ◽  
Author(s):  
Tianyi Li ◽  
Angelo Arleo ◽  
Denis Sheynikhovich
Keyword(s):  
Experimental Studies ◽  
Place Cells ◽  
Grid Cells ◽  
Visual Cue ◽  
Conflicting Evidence ◽  
Hexagonal Grids ◽  
Continuous Interaction ◽  
Self Motion ◽  
High Degree ◽  
Key Parameter

AbstractHippocampal place cells and entorhinal grid cells are thought to form a representation of space by integrating internal and external sensory cues. Experimental studies show that different subsets of place cells are controlled by vision, self-motion or a combination of both. Moreover, recent studies in environments with a high degree of visual aliasing suggest that a continuous interaction between place cells and grid cells can result in a deformation of hexagonal grids or in a progressive loss of visual cue control. The computational nature of such a bidirectional interaction remains unclear. In this work we present a neural network model of a dynamic loop between place cells and grid cells. The model is tested in two recent experimental paradigms involving double-room environments that provide conflicting evidence about visual cue control over self-motion-based spatial codes. Analysis of the model behavior in the two experiments suggests that the strength of hippocampal-entorhinal dynamical loop is the key parameter governing differential cue control in multi-compartment environments. Construction of spatial representations in visually identical environments requires weak visual cue control, while synaptic plasticity is regulated by the mismatch between visual- and self-motion representations. More gener-ally our results suggest a functional segregation between plastic and dynamic processes in hippocampal processing.

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 Neural dynamics indicate parallel integration of environmental and self-motion information by place and grid cells

2019 ◽  
Author(s):  
Dmitri Laptev ◽  
Neil Burgess
Keyword(s):  
Path Integration ◽  
Temporal Dynamics ◽  
Hippocampal Formation ◽  
Place Cell ◽  
Place Cells ◽  
Motion Information ◽  
Grid Cells ◽  
Attractor Network ◽  
Cell Firing ◽  
Self Motion

AbstractPlace cells and grid cells in the hippocampal formation are thought to integrate sensory and self-motion information into a representation of estimated spatial location, but the precise mechanism is unknown. We simulated a parallel attractor system in which place cells form an attractor network driven by environmental inputs and grid cells form an attractor network performing path integration driven by self-motion, with inter-connections between them allowing both types of input to influence firing in both ensembles. We show that such a system is needed to explain the spatial patterns and temporal dynamics of place cell firing when rats run on a linear track in which the familiar correspondence between environmental and self-motion inputs is changed (Gothard et al., 1996b; Redish et al., 2000). In contrast, the alternative architecture of a single recurrent network of place cells (performing path integration and receiving environmental inputs) cannot reproduce the place cell firing dynamics. These results support the hypothesis that grid and place cells provide two different but complementary attractor representations (based on self-motion and environmental sensory inputs respectively). Our results also indicate the specific neural mechanism and main predictors of hippocampal map realignment and make predictions for future studies.

The transformation from grid cells to place cells is robust to noise in the grid pattern

Hippocampus ◽  
2014 ◽  
Vol 24 (8) ◽  
pp. 912-919 ◽  
Author(s):  
Amir H. Azizi ◽  
Natalie Schieferstein ◽  
Sen Cheng
Keyword(s):  
Place Cells ◽  
Grid Cells ◽  
Grid Pattern ◽  
Robust To Noise

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).

Sign in / Sign up

Export Citation Format

Share Document