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Related Experiment Video

Updated: Nov 2, 2025

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

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Expectancy-based rhythmic entrainment as continuous Bayesian inference.

Jonathan Cannon1

  • 1Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Plos Computational Biology
|June 9, 2021
PubMed
Summary
This summary is machine-generated.

Humans track complex rhythms by estimating timing expectations, going beyond simple entrainment. This predictive processing model explains covert and motor rhythm tracking, including responses to timing changes and omissions.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Auditory Perception

Background:

  • Human rhythm tracking involves complex temporal structure perception, exceeding simple oscillator entrainment.
  • Existing models explain rhythm entrainment by event timing but lack computational principles for expectation-based processing.
  • Predictive processing offers a framework to understand how timing expectations shape rhythm perception and motor control.

Purpose of the Study:

  • To propose a computational framework for rhythm tracking based on predictive processing.
  • To formalize rhythm tracking as phase and tempo estimation using novel inference problems.
  • To model covert and motor rhythm tracking, incorporating uncertainty and expectation dynamics.

Main Methods:

  • Developed two inference problems: Phase Inference from Point Process Event Timing (PIPPET) and Phase and Tempo Inference (PATIPPET).
  • Derived variational solutions resembling Dynamic Attending models but including novel terms for uncertainty and expectation.
  • Modeled human rhythm tracking characteristics, including error correction sensitivity and tempo perception changes.

Main Results:

  • The proposed inference problems and their solutions explain key aspects of human rhythm tracking.
  • The model accounts for sensitivity to inter-event intervals and tempo shifts due to event omissions.
  • Novel influences on entrainment yield testable behavioral predictions and align with neurophysiological data.

Conclusions:

  • Rhythm tracking can be understood as a continuous phase and tempo estimation problem within a predictive processing framework.
  • The PIPPET and PATIPPET models offer a unified computational account of expectation-based entrainment.
  • This framework provides a basis for interpreting experimental data and developing advanced predictive models of timing and active inference.