Control of noise-induced coherent oscillations in three-neuron motifs

Abstract

The phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we investigate SISR and its control in neural network motifs made up of Morris-Lecar neurons. First, for single neurons, we show that changes in electrical autaptic parameters do not affect the degree of the coherence of the oscillations due to SISR, but rather reduces the interval of the noise intensity in which the degree is high. While changes in chemical autaptic parameters do affect both the degree of SISR and the interval of noise intensity in which the degree of SISR is high. Secondly, for single motifs, we show that the degree of SISR and the interval of the noise intensity in which this degree is high depends on the nature (electrical or chemical) of the synaptic time-delayed couplings between the neurons and the topology of the motif: (i) for all topologies of electrical motifs, the weaker couplings and the shorter the time delays between the neurons, the higher the degree of SISR and the larger the noise interval in which this degree is high; (ii) for most topologies of chemical motifs, variations in the synap-

Dynamical Phases and Resonance Phenomena in Information-Processing Recurrent Neural Networks

“…Furthermore, we found a completely new resonance phenomenon which we call import resonance, showing that the correlation or mutual information between input and the subsequent network state depends on certain control parameters (such as coupling strength) in a peak-like way. Resonance phenomena are ubiquitous in biological [37] and artificial neural networks [31,38,39] and have been shown to play a crucial role for neural information processing [40][41][42]. In particular with respect to the auditory system, it has been argued that resonance phenomena like stochastic resonance are actively exploited by the brain to maintain optimal information processing [41,[43][44][45].…”