Robust spatial memory maps encoded by networks with transient connections

Babichev, Andrey; Morozov, Dmitriy; Dabaghian, Yuri

doi:10.1371/journal.pcbi.1006433

Robust spatial memory maps encoded by networks with transient connections

Journal Article · Tue Sep 18 00:00:00 EDT 2018 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1006433· OSTI ID:1560557

Babichev, Andrey ^[1]; Morozov, Dmitriy ^[2]; ^[3]

Rice Univ., Houston, TX (United States). Dept. of Computational and Applied Mathematics
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States). Berkeley Inst. for Data Science
Univ. of Texas McGovern Medical School, Houston, TX (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1560557

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 9 Vol. 14; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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