Coherence Resonance in Random Erdös-Rényi Neural Networks: Mean-Field Theory

Additive noise is known to tune the stability of nonlinear systems. Using a network two randomly connected interacting excitatory and inhibitory neural populations driven by additive noise, we derive closed mean-field representation that captures global dynamics. Building on spectral properties Erdös-Rényi networks, dynamics are obtained via projection onto random network’s principal eigenmode. We consider Gaussian zero-mean Poisson-like stimuli neurons show these types induce coherence resonance. Specifically, stochastic stimulation induces coherent oscillations in γ-frequency range at intermediate intensity. further this valid for both partial stimulation, i.e. whenever subset stimulated only. The exposes resonance γ-range transition from stable non-oscillatory equilibrium an oscillatory saddle-node bifurcation. evaluate between non-coherent state various power spectra, Spike Field Coherence information-theoretic measures.