Grid-Cell Distortion along Geometric Borders

Hägglund, Martin; Mørreaunet, Maria; Moser, May‐Britt; Moser, Edvard I

doi:10.1016/j.cub.2019.01.074

Cited by 25 publications

(27 citation statements)

References 16 publications

(43 reference statements)

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“…Firstly, we show that local changes to the grid scale 26 Secondly, since Hebbian learned connection weights between place cells reflect the correlation in their firing, and therefore their statistical discriminability 46 , two place cells with broad receptive fields would develop a stronger connection than a pair with equal separation but narrower receptive fields (stronger connections correspond to shorter distances on the grid module, producing grid patterns with larger scales in the environment; Fig. 1D).…”

Section: Online and Offline Perceptual Warping In Spatial Representatmentioning

confidence: 79%

“…With increasing experience of an environment, grid firing patterns exhibit both local scale changes 26 and global shear-like distortions 29 , the latter associated with 7.5-8° offsets of one of the grid axes 29,44 to the walls of square environments. Both effects were present in our simulations and can be attributed distinctly to the offline map-learning and online localization components of our theoretical framework.…”

Section: Online and Offline Perceptual Warping In Spatial Representatmentioning

confidence: 99%

“…Our framework also provides algorithmic-and implementation-level explanations for observed features of grid cell firing in response to manipulations 7,9,[21][22][23][24] or inhomogeneity [25][26][27][28][29] of environmental sensory input. Overall, these phenomena can be understood in a probabilistic framework, where minimization of prediction errors between the transition and observation models are traded against prior model beliefs.…”

Section: Introductionmentioning

confidence: 99%

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Replay as structural inference in the hippocampal-entorhinal system

Evans¹,

Burgess²

2020

Preprint

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Model-based decision making relies on the construction of an accurate representation of the underlying state-space, and localization of one’s current state within it. One way to localize is to recognize the state with which incoming sensory observations have been previously associated. Another is to update a previous state estimate given a known transition. In practice, both strategies are subject to uncertainty and must be balanced with respect to their relative confidences; robust learning requires aligning the predictions of both models over historic observations. Here, we propose a dual-systems account of the hippocampal-entorhinal system, where sensory prediction errors between these models during online exploration of state space initiate offline probabilistic inference. Offline inference computes a metric embedding on grid cells of an associative place graph encoded in the recurrent connections between place cells, achieved by message passing between cells representing non-local states. We provide testable explanations for coordinated place and grid cell ‘replay’ as efficient message passing, and for distortions, partial rescaling and direction-dependent offsets in grid patterns as the confidence weighted balancing of model priors, and distortions to grid patterns as reflecting inhomogeneous sensory inputs across states.Author SummaryMinimising prediction errors between transition and sensory input (observation) models predicts partial rescaling and direction-dependent offsets in grid cell firing patterns.Inhomogeneous sensory inputs predict distortions of grid firing patterns during online localisation, and local changes of grid scale during offline inference.Principled information propagation during offline inference predicts coordinated place and grid cell ‘replay’, where sequences propagate between structurally related features.

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Section: Online and Offline Perceptual Warping In Spatial Representatmentioning

confidence: 79%

Section: Online and Offline Perceptual Warping In Spatial Representatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Replay as structural inference in the hippocampal-entorhinal system

Evans¹,

Burgess²

2020

Preprint

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“…The simulated animal 151 moved with anisotropic behavior so that whenever it reached a wall there was 0.02 higher probability 152 per iteration of turning to the left than the right, which also leads to a higher average velocity and 153 higher occupancy along the walls (Figure 2A). This stereotypical behavior of rats is correlated to 154 compression distortions (Hagglund et al 2019). 155…”

Section: An Anchoring Can Model Recapitulates Field Movement and Distmentioning

confidence: 99%

“…The grid pattern is not perfectly hexagonal but displays 29 elastic distortions that shears the grid along the orientation of a square environment (Stensola et al 30 2015). Moreover, local elastic distortions have been described that stems from an interaction with the 31 borders of the environment, compressing the grid along the axes of the walls (Hagglund et al 2019). 32…”

Section: Introduction 19mentioning

confidence: 99%

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

Hägglund

Mørreaunet

2019

Preprint

Self Cite

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The 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.

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Untethered firing fields and intermittent silences: Why grid‐cell discharge is so variable

2020

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Grid cells in medial entorhinal cortex are notoriously variable in their responses, despite the striking hexagonal arrangement of their spatial firing fields. Indeed, when the animal moves through a firing field, grid cells often fire much more vigorously than predicted or do not fire at all. The source of this trial‐to‐trial variability is not completely understood. By analyzing grid‐cell spike trains from mice running in open arenas and on linear tracks, we characterize the phenomenon of “missed” firing fields using the statistical theory of zero inflation. We find that one major cause of grid‐cell variability lies in the spatial representation itself: firing fields are not as strongly anchored to spatial location as the averaged grid suggests. In addition, grid fields from different cells drift together from trial to trial, regardless of whether the environment is real or virtual, or whether the animal moves in light or darkness. Spatial realignment across trials sharpens the grid representation, yielding firing fields that are more pronounced and significantly narrower. These findings indicate that ensembles of grid cells encode relative position more reliably than absolute position.

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Grid-Cell Distortion along Geometric Borders

Cited by 25 publications

References 16 publications

Replay as structural inference in the hippocampal-entorhinal system

Replay as structural inference in the hippocampal-entorhinal system

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

Untethered firing fields and intermittent silences: Why grid‐cell discharge is so variable

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