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 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.

Oscillator-Interference Models of Path Integration Do Not Require Theta Oscillations

2015 ◽  
Vol 27 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Jeff Orchard
Keyword(s):  
Path Integration ◽  
Activity Patterns ◽  
Field Potential ◽  
Grid Cell ◽  
Place Cells ◽  
Theta Oscillations ◽  
Grid Cells ◽  
Neural Oscillators ◽  
Theta Frequency ◽  
Interference Models

Navigation and path integration in rodents seems to involve place cells, grid cells, and theta oscillations (4–12 Hz) in the local field potential. Two main theories have been proposed to explain the neurological underpinnings of how these phenomena relate to navigation and to each other. Attractor network (AN) models revolve around the idea that local excitation and long-range inhibition connectivity can spontaneously generate grid-cell-like activity patterns. Oscillator interference (OI) models propose that spatial patterns of activity are caused by the interference patterns between neural oscillators. In rats, these oscillators have a frequency close to the theta frequency. Recent studies have shown that bats do not exhibit a theta cycle when they crawl, and yet they still have grid cells. This has been interpreted as a criticism of OI models. However, OI models do not require theta oscillations. We explain why the absence of theta oscillations does not contradict OI models and discuss how the two families of models might be distinguished experimentally.

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 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.

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 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 A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kun Han ◽  
Dewei Wu ◽  
Lei Lai
Keyword(s):  
Intelligent Agents ◽  
Autonomous Navigation ◽  
Spatial Representation ◽  
Information Source ◽  
Grid Cell ◽  
Place Cells ◽  
Space Representation ◽  
Generation Model ◽  
Grid Cells ◽  
Representation Model

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.

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.

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