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How Entorhinal Grid Cells May Learn Multiple Spatial Scales from a Dorsoventral Gradient of Cell Response Rates in a Self-organizing Map

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
How Entorhinal Grid Cells May Learn Multiple Spatial Scales from a Dorsoventral Gradient of Cell Response Rates in a Self-organizing Map
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002648
Pubmed ID
Authors

Stephen Grossberg, Praveen K. Pilly

Abstract

Place cells in the hippocampus of higher mammals are critical for spatial navigation. Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex (MEC) input to place cells. Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis. Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells. But how do grid cells learn to fire at multiple positions that form a hexagonal grid, and with spatial scales that increase along the dorsoventral axis? In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs) whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs), both increase along this axis. Slower (faster) subthreshold MPOs and slower (faster) EPSPs correlate with larger (smaller) grid spacings and field widths. A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales, which perform linear velocity path integration. The model cells also exhibit MPO frequencies that covary with their response rates. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing. A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic "neural relativity" that may clarify how episodic memories are learned.

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Geographical breakdown

Country Count As %
United States 6 6%
United Kingdom 3 3%
Germany 2 2%
Canada 2 2%
France 1 1%
Netherlands 1 1%
Unknown 84 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 23%
Researcher 20 20%
Student > Master 10 10%
Student > Doctoral Student 8 8%
Student > Bachelor 7 7%
Other 18 18%
Unknown 13 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 36%
Neuroscience 14 14%
Medicine and Dentistry 8 8%
Computer Science 8 8%
Psychology 7 7%
Other 12 12%
Unknown 14 14%