Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.
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Brunel, Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons, Journal of Computational Neuroscience, 8 (2000), pp
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A novel MPI-based construction method for spiking neural networks on multi-GPU clusters is introduced, with scaling demonstrated on two cortical models using point-to-point and collective communication.
Mean-field theory of soft-threshold integrate-and-fire networks predicts Hopf, Turing, and Turing-Hopf bifurcations producing oscillations, bumps, and spatiotemporal waves, confirmed via stochastic simulations.
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From Chaos to Synchrony in Recurrent Excitatory-Inhibitory Networks with Target-Specific Inhibition
Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.
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Scalable Construction of Spiking Neural Networks using up to thousands of GPUs
A novel MPI-based construction method for spiking neural networks on multi-GPU clusters is introduced, with scaling demonstrated on two cortical models using point-to-point and collective communication.
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Coherent dynamics in soft-threshold integrate-and-fire networks
Mean-field theory of soft-threshold integrate-and-fire networks predicts Hopf, Turing, and Turing-Hopf bifurcations producing oscillations, bumps, and spatiotemporal waves, confirmed via stochastic simulations.