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

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

Research output: Contribution to journalComment/debate

66 Scopus citations

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 independentneuron models based on temporal interference; and to suggest open questions for experiment and theory.

Original languageAmerican English
Pages (from-to)1283-1300
Number of pages18
JournalHippocampus
Volume18
Issue number12
DOIs
StatePublished - Dec 2008
Externally publishedYes

Keywords

  • Encoding/decoding
  • Entorhinal cortex
  • Grid cells
  • Hippocampus
  • Navigation
  • Place cells
  • Theory

Fingerprint

Dive into the research topics of 'Grid cells: The position code, neural network models of activity, and the problem of learning'. Together they form a unique fingerprint.

Cite this