Noise-tuned bursting in a Hedgehog burster

Area of Science:

• Computational Neuroscience
• Nonlinear Dynamics
• Neuronal Firing Patterns

Background:

• Neuronal firing behavior is significantly influenced by intrinsic noise.
• The Hedgehog burster model exhibits complex dynamics susceptible to noise perturbations.

Purpose of the Study:

• To investigate the role of noise in modulating spike counts of neuronal bursts.
• To explore the phenomenon of self-induced stochastic resonance (SISR) in neuronal models.

Main Methods:

• Analysis of the Hedgehog burster model under varying noise strengths.
• Utilizing the distance matching condition to predict critical transitions on slow manifolds.
• Simulating stochastic periodic orbits and analyzing noise-induced trapping.

Main Results:

• Noise effectively tunes spike counts in bursts through SISR.
• Critical transition positions on the slow manifold show a staircase-like dependence on noise strength.
• Noise-induced trapping of the slow variable is observed, increasing with noise intensity.

Conclusions:

• SISR provides a mechanism for noise-induced tuning of neuronal burst properties.
• The observed phenomena, including staircase dependence and noise-induced trapping, highlight the robustness of SISR.
• These findings offer insights into how noise shapes neuronal network function and information processing.