Chaotic resonance in hybrid scale-free neural networks.

It has been recently shown that weak signal response of a neuron can be amplified with the help of chaotic fluctuations at an optimal intensity. This phenomenon is referred to as chaotic resonance (CR). Here, we numerically investigate the signal detection ability of nervous system via CR using hybrid coupled scale-free networks of Hodgkin-Huxley (H-H) neurons. Firstly, we find that chaotic internal fluctuations are prone to enhance signal response of neuron population, even with a weak connectivity, more than that of single isolated cell's. We show that gap junctions can satisfy the stability against such variability and the quality of perception due to synchronizability. We also show that intense hybrid network interaction with strong chemical coupling can induce the similar degree of stability and decent quality of CR performance. Our results imply that a balanced connectivity may respond to weak signals efficiently in the presence of optimal chaotic fluctuations and exhibit frequency selectivity. • CR performance is enhanced in the presence of network interaction. • Gap junctions in hybrid coupled population promote more pronounced perception. • Strongly coupled network increases sensitivity and precision on frequency modulation. [ABSTRACT FROM AUTHOR]