Adjoint Method for Macroscopic Phase-Resetting Curves of Generic Spiking Neural Networks

TL;DR

This paper introduces an adjoint method within a refractory density framework to compute macroscopic phase-resetting curves of neural networks, linking individual neuron properties to collective oscillations.

Contribution

It develops a novel adjoint approach for the refractory density equation to analytically derive the PRC of generic spiking neural networks.

Findings

Validates the framework with neural network examples

Links neuron properties to collective oscillation features

Provides semi-analytical expression for PRC

Abstract

Brain rhythms emerge as a result of synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the macroscopic PRC and developing a systematic phase reduction theory for emerging rhythms remains an outstanding theoretical challenge. Here we present a practical theoretical framework to compute the PRC of generic spiking networks with emergent collective oscillations. To do so, we adopt a refractory density approach where neurons are described by the time since their last action potential. In the thermodynamic limit, the network dynamics are captured by a continuity equation known as the refractory density equation. We develop an appropriate adjoint method for this equation which in turn gives a semi-analytical expression of the infinitesimal PRC. We confirm the validity of our framework for specific examples of neural networks. Our theoretical findings highlight the relationship between key biological properties at the individual neuron scale and the macroscopic oscillatory properties assessed by the PRC.