Deforming the hippocampal map

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...

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Bibliographic Details
Published in:	Hippocampus Vol. 15; no. 1; pp. 41 - 55
Main Authors:	Touretzky, David S., Weisman, Wendy E., Fuhs, Mark C., Skaggs, William E., Fenton, Andre A., Muller, Robert U.
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 2005
Subjects:	Action Potentials - physiology Animals attractor network model Brain Mapping hippocampal map Hippocampus - physiology maximum likelihood estimation Nerve Net - physiology Neural Inhibition - physiology Neural Networks (Computer) Neural Pathways - physiology Neurons - physiology Nonlinear Dynamics Orientation - physiology Rats Space Perception - physiology
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Summary:	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 , 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.
Bibliography:	National Science Foundation - No. IIS-9978403; No. DGE-9987588 istex:142B86232460BA372CC60E8A8C5F32461F682D65 ark:/67375/WNG-J62Z55H4-S ArticleID:HIPO20029 National Institutes of Health - No. MH59932; No. NS20686; No. NS37150 Medical Research Council ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1050-9631 1098-1063
DOI:	10.1002/hipo.20029