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

Grieves, Roddy M.; Jedidi-Ayoub, Selim; Mishchanchuk, Karyna; Liu, Anyi; Renaudineau, Sophie; Duvelle, Éléonore; Jeffery, Kate J.

doi:10.1038/s41593-021-00907-4

Cited by 53 publications

(47 citation statements)

References 41 publications

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“…In addition to the typical calculation of the two metrics, we perform standardization similar to Grieves et al (2021). First, the spatial information and sparsity index of the original spike train of each neuron in each trial is achieved.…”

Section: Sparsity Index and Spatial Informationmentioning

confidence: 99%

“…We follow the method specified by Stella and Treves (2015) and Grieves et al (2021) to perform planar symmetry analysis on the prototypical structures and adjust the HCP, FCC, and COL scores (χ HCP , χ FCC , and χ COL , respectively) based on the heatmaps of hexagonal grid scores and squared grid scores (Supplementary Figure S1A). Each pixel of the heatmap is the grid score of an oblique slice of the 3D autocorrelation.…”

Section: Structure Scoresmentioning

confidence: 99%

“…During volumetric navigation, grid fields at least preserve local order. Two latest studies on rats navigating in a cubic lattice and bats flying in a rectangular room report that the volumetric grid fields do not fit into HCP, FCC, or COL, but are more regular than a random organization of fields (RND) (Ginosar et al, 2021;Grieves et al, 2021). On the contrary, another study implies that FCC could explain the fMRI activities of the entorhinal cortex of human subjects navigating in a 3D virtual reality paradigm (Kim and Maguire, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, recordings of grid cells on sloped, multilayered and volumetric environments (Hayman et al, 2011(Hayman et al, , 2015Casali et al, 2019;Kim and Maguire, 2019;Ginosar et al, 2021;Grieves et al, 2021) challenge the theoretical prediction of isotropic 3D grid fields. Hayman et al (2011) uncovered that when navigating on a pegboard and a helix, rats manifest vertically elongated grid fields; this anisotropy suggests columnar fields (COL) in 3D.…”

Section: Introductionmentioning

confidence: 99%

“…A perceptual error occurs due to the limitation in the sensors, cognitive errors, to name a few. The error could perturb the RNN updates and explain why grid fields lose global order in volumetric navigation reported by Grieves et al (2021) and Ginosar et al (2021). Third, the model allows switching among navigation modes categorized by plane definition, grid cell periodicity, perceptual error, etc.…”

Section: Introductionmentioning

confidence: 99%

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A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes

Gong

Fukunishi

2021

Front. Comput. Neurosci.

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Grid cells are crucial in path integration and representation of the external world. The spikes of grid cells spatially form clusters called grid fields, which encode important information about allocentric positions. To decode the information, studying the spatial structures of grid fields is a key task for both experimenters and theorists. Experiments reveal that grid fields form hexagonal lattice during planar navigation, and are anisotropic beyond planar navigation. During volumetric navigation, they lose global order but possess local order. How grid cells form different field structures behind these different navigation modes remains an open theoretical question. However, to date, few models connect to the latest discoveries and explain the formation of various grid field structures. To fill in this gap, we propose an interpretive plane-dependent model of three-dimensional (3D) grid cells for representing both two-dimensional (2D) and 3D space. The model first evaluates motion with respect to planes, such as the planes animals stand on and the tangent planes of the motion manifold. Projection of the motion onto the planes leads to anisotropy, and error in the perception of planes degrades grid field regularity. A training-free recurrent neural network (RNN) then maps the processed motion information to grid fields. We verify that our model can generate regular and anisotropic grid fields, as well as grid fields with merely local order; our model is also compatible with mode switching. Furthermore, simulations predict that the degradation of grid field regularity is inversely proportional to the interval between two consecutive perceptions of planes. In conclusion, our model is one of the few pioneers that address grid field structures in a general case. Compared to the other pioneer models, our theory argues that the anisotropy and loss of global order result from the uncertain perception of planes rather than insufficient training.

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Section: Sparsity Index and Spatial Informationmentioning

confidence: 99%

Section: Structure Scoresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes

Gong

Fukunishi

2021

Front. Comput. Neurosci.

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Unweaving the Cognitive Map: A Personal History

Jeffery

2024

Hippocampus

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I have been incredibly fortunate to have worked in the field of hippocampal spatial coding during three of its most exciting decades, the 1990s, 2000s, and 2010s. During this time I had a ringside view of some of the foundational discoveries that were made which have transformed our understanding of the hippocampal system and its role in cognition (especially spatial cognition) and memory. These discoveries inspired me in my own lab over the years to pursue three broad lines of enquiry—3D spatial encoding, context and the sense of direction—which are outlined here. If some of my personal recollections are a little inaccurate (such is the nature of episodic memory!) I apologize in advance.

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How environmental movement constraints shape the neural code for space

Jeffery

2021

Cogn Process

Self Cite

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Study of the neural code for space in rodents has many insights to offer for how mammals, including humans, construct a mental representation of space. This code is centered on the hippocampal place cells, which are active in particular places in the environment. Place cells are informed by numerous other spatial cell types including grid cells, which provide a signal for distance and direction and are thought to help anchor the place cell signal. These neurons combine self-motion and environmental information to create and update their map-like representation. Study of their activity patterns in complex environments of varying structure has revealed that this "cognitive map" of space is not a fixed and rigid entity that permeates space, but rather is variably affected by the movement constraints of the environment. These findings are pointing toward a more flexible spatial code in which the map is adapted to the movement possibilities of the space. An as-yet-unanswered question is whether these different forms of representation have functional consequences, as suggested by an enactivist view of spatial cognition.

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

Cited by 53 publications

References 41 publications

A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes

A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes

Unweaving the Cognitive Map: A Personal History

How environmental movement constraints shape the neural code for space

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