Deforming the hippocampal map
Article first published online: 8 JUN 2004
Copyright © 2004 Wiley-Liss, Inc.
Volume 15, Issue 1, pages 41–55, 2005
How to Cite
Touretzky, D. S., Weisman, W. E., Fuhs, M. C., Skaggs, W. E., Fenton, A. A. and Muller, R. U. (2005), Deforming the hippocampal map. Hippocampus, 15: 41–55. doi: 10.1002/hipo.20029
- Issue published online: 14 FEB 2005
- Article first published online: 8 JUN 2004
- Manuscript Accepted: 9 APR 2004
- National Institutes of Health. Grant Numbers: MH59932, NS20686, NS37150
- National Science Foundation. Grant Numbers: IIS-9978403, DGE-9987588
- Medical Research Council
- hippocampal map;
- attractor network model;
- maximum likelihood estimation
To investigate conjoint stimulus control over place cells, Fenton et al. (J Gen Physiol 116:191–209, 2000a) recorded while rats foraged in a cylinder with 45° black and white cue cards on the wall. Card centers were 135° apart. In probe trials, the cards were rotated together or apart by 25°. Firing field centers shifted during these trials, stretching and shrinking the cognitive map. Fenton et al. (2000b) described this deformation with an ad hoc vector field equation. We consider what sorts of neural network mechanisms might be capable of accounting for their observations. In an abstract, maximum likelihood formulation, the rat's location is estimated by a conjoint probability density function of landmark positions. In an attractor neural network model, recurrent connections produce a bump of activity over a two-dimensional array of cells; the bump's position is influenced by landmark features such as distances or bearings. If features are chosen with appropriate care, the attractor network and maximum likelihood models yield similar results, in accord with previous demonstrations that recurrent neural networks can efficiently implement maximum likelihood computations (Pouget et al. Neural Comput 10:373–401, 1998; Deneve et al. Nat Neurosci 4:826–831, 2001). © 2004 Wiley-Liss, Inc.