Detecting the subthreshold signal in a neural network via statistical complexity measure

This paper proposes an information theory approach for detecting the subthreshold signal in a small-world network composed of Fitz Hugh-Nagumo (FHN) neurons. Statistical complexity measure (SCM) and normalized Shannon-entropy (NSE) have been defined based on the specific and nonconsecutive firing time intervals series, and employed to quantify the stochastic multiresonance (SMR) phenomena in this small-world neural network. The results show that there are several maxima of SCM and several minima of NSE at various optimal noise levels, which is regarded as the signature of the occurrence of SMR. This also implies that the subthreshold signal can be accurately detected across multiple levels of noise. More intriguingly, we have discovered that the degree of this neural network can induce the generation of multiple resonance-like behaviors. In addition, our findings demonstrate that the SCM outperforms the traditional signal-to-noise ratio (SNR) in accurately identifying firing dynamical regularity under certain noisy conditions. Furthermore, the SCM can capture subtle resonance-like behavior induced by the degree, which the SNR is unable to detect. Thus, the SCM could have potential power on detecting weak signals in neural networks.