title: Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.
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.
authors.affiliation: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA.
authors.forename: Ryan
authors.initials: R
authors.lastname: Pyle
authors.affiliation: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA.|Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana 46556, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Rosenbaum
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D003198:Computer Simulation; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*