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Computation is concentrated in rich clubs of local cortical networks
Author(s) -
Samantha P. Faber,
Nicholas M. Timme,
John M. Beggs,
Ehren L. Newman
Publication year - 2018
Publication title -
network neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.128
H-Index - 18
ISSN - 2472-1751
DOI - 10.1162/netn_a_00069
Subject(s) - computation , club , computer science , biological neural network , neuron , models of neural computation , process (computing) , artificial neural network , neuroscience , theoretical computer science , artificial intelligence , algorithm , psychology , machine learning , biology , anatomy , operating system
To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit organization. Rich clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average ∼300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute ∼160% more information than neurons outside of the rich club. The amount of computation performed in the rich club was proportional to the amount of information propagation by the same neurons. This suggests that in these circuits, information propagation drives computation. In total, our findings indicate that rich-club organization in effective cortical circuits supports not only information propagation but also neural computation.

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