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Robustness of spatial learning in flickering networks

It is widely accepted that the network of the hippocampal place cells provides a substrate of the "cognitive map" of the environment. However, thousands of hippocampal neurons die every day and the networks formed by these cells constantly change due to various forms of synaptic plasticity. What then explains the remarkable reliability of our spatial memories? We propose a computational approach to answering this question based on a couple of insights. First, we propose that the hippocampal cognitive map is fundamentally topological, i.e., more similar to a subway map than to a topographical city map [1], and hence it is amenable to analysis by topological methods [2, 3]. We then apply several novel methods from homology theory, to understand how dynamic connections between cells influences the speed and reliability of spatial learning. We simulate the rat's exploratory movements through different environments and study how topological invariants (stable topological features of different experimental environments) arise in a network of simulated neurons with dynamic, "flickering" connectivity. We find that despite transient connectivity the network of place cells produces a stable representation of the topology of the environment.


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Work is supported by the NSF grant NSF 1422438.

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Correspondence to Yuri A Dabaghian.

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

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Dabaghian, Y.A., Chowdhury, S., Babichev, A. et al. Robustness of spatial learning in flickering networks. BMC Neurosci 16 (Suppl 1), P43 (2015).

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