Stochastic and coherence resonance in feed-forward-loop neuronal network motifs

Area of Science:

• Computational Neuroscience
• Complex Systems Dynamics
• Neural Signal Processing

Background:

• Understanding how neuronal ensembles process signals is crucial for deciphering brain mechanisms.
• Noise is often viewed as detrimental, but its role in neural dynamics is increasingly recognized.

Purpose of the Study:

• To systematically investigate stochastic resonance (SR) and coherence resonance (CR) in triple-neuron feed-forward-loop (FFL) network motifs.
• To explore how noise intensity and coupling strength influence dynamic behaviors in FFLs.

Main Methods:

• Computational modeling using the Izhikevich neuron model.
• Construction and simulation of various chemical-coupled FFL network motifs.
• Analysis of noise effects on network dynamics, including SR and CR phenomena.

Main Results:

• FFL motifs can exploit noise to enhance their dynamic performance.
• SR and CR are observable in many FFL types with optimized noise intensities.
• Coupling strength acts as a critical control parameter for stochastic dynamics in FFLs.

Conclusions:

• Neuronal FFLs exhibit noise-induced signal processing capabilities, demonstrating both SR and CR.
• The findings highlight the functional role of noise in neural networks and the importance of synaptic coupling.
• Results offer insights into biological implications for neural signal transmission and brain function.