Enhanced resonance by coupling and summing in sinusoidally driven chaotic neural networks

Enhancement of resonance is shown by coupling and summing in sinusoidally d riven chaotic neural networks. This resonance phenomenon has a peak at a dr ive frequency similar to noise-induced stochastic resonance (SR), however, the mechanism is different from noise-induced SR. We numerically study the properties of resonance in chaotic neural networks in the turbulent phase w ith summing and homogeneous coupling, with particular consideration of enha ncement of the signal-to-noise ratio (SNR) by coupling and summing. Summing networks can enhance the SNR of a mean field based on the law of large num bers. Global coupling can enhance the SNR of a mean field and a neuron in t he network. However, enhancement is not guaranteed and depends on the param eters. A combination of coupling and summing enhances the SNR, but summing to provide a mean held is more effective than coupling on a neuron level to promote the SNR. The global coupling network has a negative correlation be tween the SNR of the mean field and the Kolmogorov-Sinai (KS) entropy, and between the SNR of a neuron in the network and the KS entropy. This negativ e correlation is similar to the results of the driven single neuron model. The SNR is saturated as an increase in the drive amplitude, and further inc reases change the state into a nonchaotic one. The SNR is enhanced around a few frequencies and the dependence on frequency is clearer and smoother th an the results of the driven single neuron model. Such dependence on the dr ive amplitude and frequency exhibits similarities to the results of the dri ven single neuron model. The nearest neighbor coupling network with a perio dic or free boundary can also enhance the SNR of a neuron depending on the parameters. The network also has a negative correlation between the SNR of a neuron and the KS entropy whenever the boundary is periodic or free. The network with a free boundary does not have a significant effect on the SNR from both edges of the free boundaries.