Functional phase response curves: a method for understanding synchronization of adapting neurons

Area of Science:

• Computational Neuroscience
• Neural Oscillations
• Systems Neuroscience

Background:

• Phase response curves (PRCs) are crucial for predicting neural synchrony in coupled neuron networks.
• Traditional methods using single-pulse perturbations do not fully capture the dynamics of adapting neurons.

Purpose of the Study:

• To develop and validate a new method, functional phase response curves (fPRCs), to analyze neural network synchrony in the presence of adaptation.
• To investigate how adaptation affects neural oscillator dynamics and network behavior.

Main Methods:

• Generated fPRCs using trains of pulses applied at a fixed delay after each spike, measuring convergence of the stimulus-spike relationship.
• Recorded experimental fPRCs in Aplysia pacemaker neurons and compared them to single-pulse PRCs.
• Developed a stability criterion by linearizing a coupled map based on fPRCs.
• Tested fPRC-based criteria in simulated two-neuron networks with reciprocal inhibition or excitation.

Main Results:

• Experimental fPRCs in Aplysia neurons differed significantly from single-pulse PRCs, primarily due to neuronal adaptation.
• Adaptation slowed the convergence of recovery intervals at certain phases, impacting phase resetting.
• fPRCs accurately predicted the existence and stability of 1:1 phase-locked activity in coupled model neurons.
• The derived stability criteria were successfully validated in simulations.

Conclusions:

• Functional phase response curves (fPRCs) provide a more accurate method for analyzing neural network synchrony, especially in the presence of neuronal adaptation.
• fPRCs are the first PRC-based tool capable of incorporating adaptation into the analysis of neural oscillator networks.
• This approach enhances our understanding of how adaptation influences the collective dynamics of neural circuits.