Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

Ryan Pyle and Robert Rosenbaum
Phys. Rev. Lett. 118, 018103 – Published 6 January 2017
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Abstract

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

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  • Received 26 August 2016

DOI:https://doi.org/10.1103/PhysRevLett.118.018103

© 2017 American Physical Society

Physics Subject Headings (PhySH)

NetworksPhysics of Living Systems

Authors & Affiliations

Ryan Pyle1 and Robert Rosenbaum1,2

  • 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA
  • 2Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana 46556, USA

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Issue

Vol. 118, Iss. 1 — 6 January 2017

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