Phase-locking patterns in a resonate and fire neural model with periodic drive

Area of Science:

• Computational Neuroscience
• Nonlinear Dynamics
• Biophysics

Background:

• Neurophysiological systems exhibit complex firing patterns.
• Phase-locking phenomena are crucial for neural information processing.
• Understanding neural dynamics requires robust mathematical models.

Purpose of the Study:

• To investigate phase-locking phenomena in a novel neural model (LFHN).
• To analyze the impact of time-dependent modulation on neural firing.
• To establish conditions for firing activity and assess model's applicability to experimental data.

Main Methods:

• Linearization of the FitzHugh-Nagumo model near a hyperbolic fixed point.
• Integration of an integrate-and-fire mechanism for spike generation.
• Application of circle map analysis tools to study phase-locking patterns under periodic perturbations.

Main Results:

• The LFHN model exhibits diverse phase-locking patterns with periodic perturbations.
• Modulation amplitude and frequency significantly influence phase-locking properties.
• Phase-locking patterns persist under moderate noise levels, with smooth changes in rotation number.

Conclusions:

• The forced LFHN model demonstrates robust phase-locking dynamics.
• The model's ability to capture firing statistics suggests its utility for fitting experimental neural data.
• This research provides insights into neural synchronization and information coding.