Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88167
Title: Statistical complexity is maximized in a small-world brain
Authors: Tan, Teck Liang
Cheong, Siew Ann
Keywords: Information Processing
Time Series Analysis
Issue Date: 2017
Source: Tan, T. L., & Cheong, S. A. (2017). Statistical complexity is maximized in a small-world brain. PLOS ONE, 12(8), e0183918-.
Series/Report no.: PLOS ONE
Abstract: In this paper, we study a network of Izhikevich neurons to explore what it means for a brain to be at the edge of chaos. To do so, we first constructed the phase diagram of a single Izhikevich excitatory neuron, and identified a small region of the parameter space where we find a large number of phase boundaries to serve as our edge of chaos. We then couple the outputs of these neurons directly to the parameters of other neurons, so that the neuron dynamics can drive transitions from one phase to another on an artificial energy landscape. Finally, we measure the statistical complexity of the parameter time series, while the network is tuned from a regular network to a random network using the Watts-Strogatz rewiring algorithm. We find that the statistical complexity of the parameter dynamics is maximized when the neuron network is most small-world-like. Our results suggest that the small-world architecture of neuron connections in brains is not accidental, but may be related to the information processing that they do.
URI: https://hdl.handle.net/10356/88167
http://hdl.handle.net/10220/44576
DOI: http://dx.doi.org/10.1371/journal.pone.0183918
Rights: © 2017 Tan, Cheong. 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 author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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