Published December 2008
| public
Journal Article
Grid cells: The position code, neural network models of activity, and the problem of learning
- Creators
- Welinder, Peter
- Burak, Yoram
- Fiete, Ila R.
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.
Additional Information
© 2008 Wiley-Liss. Accepted for publication 9 September 2008. Published online 19 November 2008. I.F. is grateful to Ted Brookings for numerous helpful conversations, to Adam Kepecs for a discussion on place cells and error correction, and to Kenneth Whang for pointers to the literature on SLAM. Y.B. acknowledges support from the Swartz foundation and from the National Science Foundation under Grant No. PHY05-51164.Additional details
- Eprint ID
- 13061
- DOI
- 10.1002/hipo.20519
- Resolver ID
- CaltechAUTHORS:WELhip08
- Swartz Foundation
- PHY05-51164
- National Science Foundation
- Created
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2009-01-16Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field