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

Author:

Hedrick Kathryn R.1,Zhang Kechen1

Affiliation:

1. Biomedical Engineering; Johns Hopkins University; Baltimore, Maryland

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.

Funder

DOD | Air Force Office of Scientific Research (AFOSR)

National Institute of Health

Publisher

American Physiological Society

Subject

Physiology,General Neuroscience

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