Robust spatial memory maps encoded by networks with transient connections

PLoS Comput Biol. 2018 Sep 18;14(9):e1006433. doi: 10.1371/journal.pcbi.1006433. eCollection 2018 Sep.

Abstract

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Action Potentials
  • Animals
  • Brain / physiology*
  • Brain Mapping
  • Cognition / physiology*
  • Computer Simulation
  • Hippocampus / physiology*
  • Models, Neurological*
  • Neurons / physiology
  • Poisson Distribution
  • Spatial Memory*
  • Time Factors

Grant support

This study was funded by an NSF grant 1422438. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.