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arXiv · 1611.01557

Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks

Ryan Pyle·Robert Rosenbaum
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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.

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Ryan Pyle, Robert Rosenbaum. 2016-11-04. Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks. https://doi.org/10.1103/physrevlett.118.018103

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