Macroscopic phase-resetting curves for spiking neural networks

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Brain rhythms are crucial for neural function but challenging to study in large populations.
• The phase-resetting curve (PRC) is a key tool for understanding single neuron oscillations.
• Extending PRC to macroscopic brain rhythms generated by neural networks remained unclear.

Purpose of the Study:

• To develop a framework for calculating a macroscopic phase-resetting curve (mPRC) for neural networks.
• To enable the study of macroscopic brain rhythms generated by populations of spiking neurons.
• To investigate the differential effects of stimuli on excitatory and inhibitory neuronal populations.

Main Methods:

• Utilized a thermodynamic approach combined with a reduction method.
• Simplified complex network dynamics into a system of ordinary differential equations.
• Employed the standard adjoint method to compute the macroscopic phase-resetting curve (mPRC).

Main Results:

• Successfully derived a theoretical framework for computing the mPRC in a network of spiking neurons.
• Validated theoretical findings through numerical simulations of the full spiking network.
• Demonstrated the mPRC framework's ability to predict distinct effects of transient inputs on excitatory and inhibitory neurons.

Conclusions:

• The developed mPRC framework provides a powerful tool for analyzing macroscopic brain rhythms.
• This method bridges the gap between single-neuron dynamics and population-level network behavior.
• The findings offer new insights into how neural network structure influences rhythmic activity and response to stimuli.