Premium
Grid cells: The position code, neural network models of activity, and the problem of learning
Author(s) -
Welinder Peter E.,
Burak Yoram,
Fiete Ila R.
Publication year - 2008
Publication title -
hippocampus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.767
H-Index - 155
eISSN - 1098-1063
pISSN - 1050-9631
DOI - 10.1002/hipo.20519
Subject(s) - computer science , grid , code (set theory) , grid cell , representation (politics) , dynamics (music) , artificial intelligence , computational model , network dynamics , neuroscience , cognitive science , theoretical computer science , psychology , mathematics , set (abstract data type) , pedagogy , geometry , politics , political science , law , programming language , discrete mathematics
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. © 2008 Wiley‐Liss, Inc.