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.

References in corpus (4)

• Balanced networks of spiking neurons with spatially dependent recurrent connections
• Chaos and reliability in balanced spiking networks with temporal drive
• Highly connected neurons spike less frequently in balanced networks
• Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies

Cited by in corpus (10)

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• Conditions for wave trains in spiking neural networks
• Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
• Pulse shape and voltage-dependent synchronization in spiking neuron networks
• Effect of Synaptic Heterogeneity on Neuronal Coordination
• Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks
• A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways