Phase-amplitude descriptions of neural oscillator models
Kyle C A Wedgwood, Kevin K Lin, R\"udiger Thul, Stephen Coombes

TL;DR
This paper extends phase oscillator models to include amplitude dynamics, enabling better analysis of neural oscillators that do not strongly attract to a limit cycle, especially under strong or pulsatile stimuli.
Contribution
It introduces a phase-amplitude framework that generalizes classical phase reduction, allowing for accurate modeling of neural oscillators with weaker attraction and complex responses.
Findings
Effective in describing responses to strong stimuli
Applicable to high-dimensional neural models
Reveals chaotic responses due to dynamical shear
Abstract
Phase oscillators are a common starting point for the reduced description of many single neuron models that exhibit a strongly attracting limit cycle. The framework for analysing such models in response to weak perturbations is now particularly well advanced, and has allowed for the development of a theory of weakly connected neural networks. However, the strong-attraction assumption may well not be the natural one for many neural oscillator models. For example, the popular conductance based Morris-Lecar model is known to respond to periodic pulsatile stimulation in a chaotic fashion that cannot be adequately described with a phase reduction. In this paper, we generalise the phase description that allows one to track the evolution of distance from the cycle as well as phase on cycle. We use a classical technique from the theory of ordinary differential equations that makes use of a…
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