Transition to chaos in random networks with cell-type-specific connectivity

2014/07/08 by Johnatan Aljadeff, Merav Stern, Tatyana O. Sharpee · 2 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Mathematics · #q-bio.NC #cond-mat.dis-nn #math.PR #nlin.CD

paper · pdf · doi:10.1103/physrevlett.114.088101

published as Phys. Rev. Lett. 114, 088101 (2015)

arxiv created 2014/07/08 · arxiv updated 2015/02/24

Abstract

In neural circuits, statistical connectivity rules strongly depend on neuronal type. Here we study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method, and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances. We apply our results to show how a small group of hyper-excitable neurons within the network can significantly increase the network's computational capacity.

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• Random neural networks match observed dimensionality of neural population recordings and motivate stronger experimental tests
• Task-specific programming of chaos in neural circuits

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