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Title: Robust spatial memory maps encoded by networks with transient connections

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.

Authors:
 [1];  [2]; ORCiD logo [3]
  1. Rice Univ., Houston, TX (United States). Dept. of Computational and Applied Mathematics
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States). Berkeley Inst. for Data Science
  3. Univ. of Texas McGovern Medical School, Houston, TX (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1560557
Grant/Contract Number:  
AC02-05CH11231; 1422438
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 14; Journal Issue: 9; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Babichev, Andrey, Morozov, Dmitriy, and Dabaghian, Yuri. Robust spatial memory maps encoded by networks with transient connections. United States: N. p., 2018. Web. doi:10.1371/journal.pcbi.1006433.
Babichev, Andrey, Morozov, Dmitriy, & Dabaghian, Yuri. Robust spatial memory maps encoded by networks with transient connections. United States. doi:10.1371/journal.pcbi.1006433.
Babichev, Andrey, Morozov, Dmitriy, and Dabaghian, Yuri. Tue . "Robust spatial memory maps encoded by networks with transient connections". United States. doi:10.1371/journal.pcbi.1006433. https://www.osti.gov/servlets/purl/1560557.
@article{osti_1560557,
title = {Robust spatial memory maps encoded by networks with transient connections},
author = {Babichev, Andrey and Morozov, Dmitriy and Dabaghian, Yuri},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1006433},
journal = {PLoS Computational Biology (Online)},
number = 9,
volume = 14,
place = {United States},
year = {2018},
month = {9}
}

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