A canonical circuit for generating phase-amplitude coupling

Angela C E Onslow, Matt Jones, Rafal Bogacz

Research output: Contribution to journalArticle (Academic Journal)peer-review

40 Citations (Scopus)

Abstract

'Phase amplitude coupling' (PAC) in oscillatory neural activity describes a phenomenon whereby the amplitude of higher frequency activity is modulated by the phase of lower frequency activity. Such coupled oscillatory activity--also referred to as 'cross-frequency coupling' or 'nested rhythms'--has been shown to occur in a number of brain regions and at behaviorally relevant time points during cognitive tasks; this suggests functional relevance, but the circuit mechanisms of PAC generation remain unclear. In this paper we present a model of a canonical circuit for generating PAC activity, showing how interconnected excitatory and inhibitory neural populations can be periodically shifted in to and out of oscillatory firing patterns by afferent drive, hence generating higher frequency oscillations phase-locked to a lower frequency, oscillating input signal. Since many brain regions contain mutually connected excitatory-inhibitory populations receiving oscillatory input, the simplicity of the mechanism generating PAC in such networks may explain the ubiquity of PAC across diverse neural systems and behaviors. Analytic treatment of this circuit as a nonlinear dynamical system demonstrates how connection strengths and inputs to the populations can be varied in order to change the extent and nature of PAC activity, importantly which phase of the lower frequency rhythm the higher frequency activity is locked to. Consequently, this model can inform attempts to associate distinct types of PAC with different network topologies and physiologies in real data.

Original languageEnglish
Pages (from-to)e102591
JournalPLoS ONE
Volume9
Issue number8
DOIs
Publication statusPublished - 2014

Keywords

  • Action Potentials
  • Brain
  • Brain Waves
  • Humans
  • Models, Neurological
  • Nerve Net
  • Neural Inhibition
  • Neurons
  • Nonlinear Dynamics

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