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A canonical circuit for generating phase-amplitude coupling.

Angela C E Onslow1, Matthew W Jones2, Rafal Bogacz3

  • 1Bristol Centre for Complexity Sciences (B.C.C.S.), University of Bristol, Queen's Building, Bristol, United Kingdom; Department of Psychology, Center for Memory and Brain, Boston University, Boston, MA, United States of America.

Plos One
|August 20, 2014
PubMed
Summary
This summary is machine-generated.

This study models how interconnected excitatory and inhibitory neural populations generate phase amplitude coupling (PAC) in brain activity. The model explains how nested rhythms arise from neural circuit dynamics and oscillatory input, clarifying PAC

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Phase amplitude coupling (PAC) is a neural phenomenon where high-frequency brain activity amplitude is modulated by low-frequency phase.
  • PAC, also known as cross-frequency coupling or nested rhythms, is observed in various brain regions and linked to cognitive tasks.
  • The precise circuit mechanisms underlying PAC generation remain largely unknown.

Purpose of the Study:

  • To present a computational model of a canonical neural circuit capable of generating PAC.
  • To elucidate how interconnected excitatory and inhibitory neural populations produce PAC.
  • To explore how varying circuit parameters influences PAC characteristics.

Main Methods:

  • Development of a canonical neural circuit model comprising interconnected excitatory and inhibitory populations.
  • Simulation of the circuit's response to oscillatory afferent drive.
  • Analytic treatment of the circuit as a nonlinear dynamical system.

Main Results:

  • The model demonstrates how periodic shifts in neural population firing patterns, driven by oscillatory input, generate PAC.
  • The model shows that interconnected excitatory-inhibitory populations can produce higher frequency oscillations phase-locked to a lower frequency input.
  • Analytic results show that connection strengths and inputs can be tuned to control PAC extent and phase-locking.

Conclusions:

  • The proposed simple circuit mechanism can explain the widespread occurrence of PAC across diverse neural systems.
  • The model provides a framework for associating specific PAC features with distinct network topologies and physiological properties.
  • This model can guide future research in analyzing real neural data to understand PAC's functional relevance.