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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
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Adaptive oscillators support Bayesian prediction in temporal processing.

Keith B Doelling1,2, Luc H Arnal1, M Florencia Assaneo3

  • 1Institut Pasteur, Université Paris Cité, Inserm UA06, Institut de l'Audition, Paris, France.

Plos Computational Biology
|November 27, 2023
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Summary
This summary is machine-generated.

Humans can predictively synchronize to rhythms. An adaptive oscillator model explains this behavior, unifying different temporal inference frameworks and suggesting neural oscillators act as physiological priors for processing noisy rhythms.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Auditory Perception

Background:

  • Humans exhibit remarkable ability in synchronizing behavior with external rhythms, crucial for activities like music and dance.
  • The neural basis of rhythmic inference is debated, with theories proposing high-level generative models versus local intrinsic oscillators.

Purpose of the Study:

  • To investigate human perception of temporally regular but variable tone sequences.
  • To model human rhythmic inference using a dynamical systems approach.

Main Methods:

  • Participants perceived tone sequences with varying rates and variability.
  • Behavior was modeled using a dynamical systems perspective, focusing on an adaptive frequency oscillator.

Main Results:

  • An adaptive frequency oscillator model successfully captured human behavior, outperforming canonical nonlinear and predictive ramping models.
  • This adaptive oscillator framework unifies previously distinct absolute and relative computational mechanisms.
  • Neural oscillators were shown to function as Bayesian priors, reducing temporal uncertainty in noisy rhythm processing.

Conclusions:

  • Adaptive oscillators offer a biologically plausible mechanism for rhythmic inference, reconciling diverse computational frameworks.
  • This research highlights the role of intrinsic neural oscillators in predictive sensory processing and temporal estimation.