Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network.

Abstract:

:The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic 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. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. 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.

journal_name

J Neurophysiol

authors

Hedrick KR,Zhang K

doi

10.1152/jn.00856.2015

subject

Has Abstract

pub_date

2016-08-01 00:00:00

pages

868-91

issue

2

eissn

0022-3077

issn

1522-1598

pii

jn.00856.2015

journal_volume

116

pub_type

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