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Journal of Computational Neuroscience

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01-12-2016

Linking dynamics of the inhibitory network to the input structure

Authors: Maxim Komarov, Maxim Bazhenov

Published in: Journal of Computational Neuroscience | Issue 3/2016

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Abstract

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives.

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Title: Linking dynamics of the inhibitory network to the input structure
Authors: Maxim Komarov
Maxim Bazhenov
Publication date: 01-12-2016
Publisher: Springer US
Published in: Journal of Computational Neuroscience / Issue 3/2016
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-016-0622-8

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