Work

Grid cells: The position code, neural network models of activity, and the problem of learning

Year: 2008

Type: review

Abstract: We review progress on the modeling and theoretical fronts in the quest to unravel the computational properties of the grid cell code and to explain the mechanisms underlying grid cell dynamics. The goals of the review are to outline a coherent framework for understanding the dynamics of grid cells and their representation of space; to critically present and draw contrasts between recurrent network models of grid cells based on continuous attractor dynamics and independent-neuron models based on temporal interference; and to suggest open questions for experiment and theory.

Source: Hippocampus

DOI: 10.1002/hipo.20519

Authors: Peter E. Welinder, Yoram Burak, Ila R. Fiete

Institutions: California Institute of Technology, Harvard University, The University of Texas at Austin

Language: English

Topic: Memory and Neural Mechanisms

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: Kavli Institute for Theoretical Physics