Journal
JOURNAL OF NEUROPHYSIOLOGY
Volume 116, Issue 2, Pages 868-891Publisher
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00856.2015
Keywords
continuous attractor; Poisson; activity bump; combinatorial mode; CA3
Categories
Funding
- Air Force Office of Scientific Research [FA9550-12-1-0018]
- National Institute of Mental Health [R01MH079511]
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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.
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