Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks (2017)
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Paper Ryan Pyle and Robert Rosenbaum, Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks, Physical Review Letters, 118, 018103 (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