HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Banquet, J. P. Articles by Burnod, Y. Search for Related Content PubMed PubMed Citation Articles by Banquet, J. P. Articles by Burnod, Y.

A Hierarchy of Associations in Hippocampo-Cortical Systems: Cognitive Maps and Navigation Strategies

banquet{at}ccr.jussieu.fr, INSERM U483 Neuroscience and Modelization, Université Pierre et Marie Curie, 75252 Paris, France

Ph. Gaussier

gaussier{at}ensea.fr, CNRS U2235 ETIS-Neurocybernétique, Université de Cergy-Pontoise-ENSEA, 95014 Cergy-Pontoise, France

M. Quoy

quoy{at}ensea.fr, CNRS U2235 ETIS-Neurocybernétique, Université de Cergy-Pontoise-ENSEA, 95014 Cergy-Pontoise, France

A. Revel

revel{at}ensea.fr, CNRS U2235 ETIS-Neurocybernétique, Université de Cergy-Pontoise-ENSEA, 95014 Cergy-Pontoise, France

Y. Burnod

ybteam{at}snv.jussieu.fr, INSERM U483 Neuroscience and Modelization, Université Pierre et Marie Curie, 75252 Paris, France

In this letter we describe a hippocampo-cortical model of spatial processing and navigation based on a cascade of increasingly complex associative processes that are also relevant for other hippocampal functions such as episodic memory. Associative learning of different types and the related pattern encoding-recognition take place at three successive levels: (1) an object location level, which computes the landmarks from merged multimodal sensory inputs in the parahippocampal cortices; (2) a subject location level, which computes place fields by combination of local views and movement-related information in the entorhinal cortex; and (3) a spatiotemporal level, which computes place transitions from contiguous place fields in the CA3-CA1 region, which form building blocks for learning temporospatial sequences.

At the cell population level, superficial entorhinal place cells encode spatial, context-independent maps as landscapes of activity; populations of transition cells in the CA3-CA1 region encode context-dependent maps as sequences of transitions, which form graphs in prefrontal-parietal cortices. The model was tested on a robot moving in a real environment; these tests produced results that could help to interpret biological data. Two different goal-oriented navigation strategies were displayed depending on the type of map used by the system.

Thanks to its multilevel, multimodal integration and behavioral implementation, the model suggests functional interpretations for largely unaccounted structural differences between hippocampo-cortical systems. Further, spatiotemporal information, a common denominator shared by several brain structures, could serve as a cognitive processing frame and a functional link, for example, during spatial navigation and episodic memory, as suggested by the applications of the model to other domains, temporal sequence learning and imitation in particular.

This article has been cited by other articles:

				J. G. Fleischer, J. A. Gally, G. M. Edelman, and J. L. Krichmar Retrospective and prospective responses arising in a modeled hippocampus during maze navigation by a brain-based device PNAS, February 27, 2007; 104(9): 3556 - 3561. [Abstract] [Full Text] [PDF]