Phase-amplitude descriptions of neural oscillator models

Wedgwood, Kyle C.A.; Lin, Kevin K.; Thul, Ruediger; Coombes, Stephen

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 moving coordinate system to analyse periodic orbits. The subsequent phase-amplitude description is shown to be very well suited to understanding the response of the oscillator to external stimuli (which are not necessarily weak). We consider a number of examples of neural oscillator models, ranging from planar through to high dimensional models, to illustrate the effectiveness of this approach in providing an improvement over the standard phase-reduction technique. As an explicit application of this phase-amplitude framework, we consider in some detail the response of a generic planar model where the strong-attraction assumption does not hold, and examine the response of the system to periodic pulsatile forcing. In addition, we explore how the presence of dynamical shear can lead to a chaotic response.

Citation

Wedgwood, K. C., Lin, K. K., Thul, R., & Coombes, S. Phase-amplitude descriptions of neural oscillator models. Journal of Mathematical Neuroscience, 3(2), https://doi.org/10.1186/2190-8567-3-2

• Journal Article Type: Article
• Deposit Date: Feb 12, 2013
• Journal: Journal of Mathematical Neuroscience
• Electronic ISSN: 2190-8567
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 2
• DOI: https://doi.org/10.1186/2190-8567-3-2
• Public URL: https://nottingham-repository.worktribe.com/output/1005729
• Publisher URL: http://www.mathematical-neuroscience.com/content/3/1/2