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 Neurophysioljournal_title
Journal of neurophysiologyauthors
Hedrick KR,Zhang Kdoi
10.1152/jn.00856.2015subject
Has Abstractpub_date
2016-08-01 00:00:00pages
868-91issue
2eissn
0022-3077issn
1522-1598pii
jn.00856.2015journal_volume
116pub_type
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