Modeling Spatial Maps Inspired by the Hippocampal System
Final rept. 1 Apr 2012-31 Mar 2015
JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF BIOMEDICAL ENGINEERING
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We propose that hippocampal networks are built upon a fundamental unit called a megamap, or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. We have also derived a set of necessary and sufficient conditions for a general class of systems that performs exact path integration, which provides an input to the megamap besides landmark cues. Our theory subsumes several existing exact path integration models, including the continuous attractor networks, as special cases. We have developed a reduction method for a class of asymmetric attractor networks that store sequences of activity patterns as associative memories.
- Anatomy and Physiology
- Medicine and Medical Research