Inverse stochastic resonance in adaptive small-world neural networks

Area of Science:

• Computational Neuroscience
• Complex Systems
• Nonlinear Dynamics

Background:

• Inverse stochastic resonance (ISR) is a phenomenon where noise optimizes oscillator frequency.
• ISR was experimentally verified in cerebellar Purkinje neurons, enhancing information transfer.
• Small-world neural networks show efficient information processing.

Purpose of the Study:

• To numerically investigate the impact of adaptive mechanisms on ISR.
• To explore ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons.
• To understand how network adaptation influences information transfer.

Main Methods:

• Simulated a small-world network of noisy FitzHugh-Nagumo neurons in a bi-metastable regime.
• Investigated dynamic network adaptation using spike-time-dependent plasticity (STDP) and homeostatic structural plasticity (HSP).
• Analyzed the influence of FHN timescale separation parameter (ε) and plasticity parameters (P, F) on ISR.

Main Results:

• ISR degree strongly depends on the FHN timescale separation parameter (ε).
• Both STDP (parameter P) and HSP (parameter F) amplify ISR within the FHN bi-stability region.
• Depression-dominant STDP (P) enhances ISR more than HSP rewiring frequency (F).

Conclusions:

• Adaptive mechanisms like STDP and HSP can significantly enhance ISR in neural networks.
• Findings offer strategies for optimizing information transfer in artificial neural circuits.
• Results guide experimental investigations into ISR in neural systems.