Detecting stochastic multiresonance in neural networks via statistical complexity measure

Area of Science:

• Neuroscience
• Complex Systems
• Signal Processing

Background:

• Stochastic multiresonance (SMR) is a phenomenon where noise enhances signal detection in nonlinear systems.
• Small-world neural networks and FitzHugh-Nagumo (FHN) neurons are models used to study complex neuronal dynamics.
• Time delay in information transmission can significantly impact neuronal network behavior.

Purpose of the Study:

• To investigate the occurrence of SMR induced by noise and time delay in small-world FHN neural networks.
• To analyze the role of statistical complexity measure (SCM) in identifying SMR.
• To explore how network parameters and time delay influence signal detection.

Main Methods:

• Employing statistical complexity measure (SCM) to quantify system complexity.
• Simulating small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons.
• Analyzing the effects of varying noise levels and time delays on neuronal firing dynamics.

Main Results:

• SCM identified quadruple stochastic resonances at four optimal noise levels.
• Delay-induced SMR was observed at multiples of the subthreshold signal period under moderate noise.
• Coherence between time delay and neuronal firing emerged at specific delays under low noise.
• Optimizing network parameters (degree, size, coupling strength) enhanced delay-induced SMR.

Conclusions:

• SCM is an effective tool for detecting SMR and understanding its mechanisms in neural networks.
• Time delay plays a crucial role in inducing multiresonance phenomena.
• Network parameter tuning can optimize SMR for improved subthreshold signal detection and information transmission.