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Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
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Multisensory integration in chaotic networks.

Adam Ponzi1, Keisuke Suzuki1

  • 1Center for Human Nature, Artificial Intelligence and Neuroscience (CHAIN), Hokkaido University, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|July 13, 2025
PubMed
Summary
This summary is machine-generated.

This study models multisensory integration, revealing that brain network chaos explains fluctuations in spatial perception and unity. Operating at the edge of chaos naturally reproduces key experimental findings in multisensory perception.

Keywords:
Bayesian Causal InferenceChaosMultisensory integrationNeural network dynamicsPerceptual illusionUnified perception

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Perception

Background:

  • Multisensory integration exhibits puzzling fluctuations in spatial perception and unity across trials.
  • Existing models struggle to explain these endogenous fluctuations, which depend on stimulus disparity and history.

Purpose of the Study:

  • To develop a minimal deterministic firing rate network model of multisensory brain dynamics.
  • To explore how chaotic network dynamics influence spatial localization belief and the perception of a unified cause.

Main Methods:

  • A general, minimal deterministic firing rate network model was developed.
  • The model's behavior was analyzed at the edge of chaos.
  • Simulations investigated the impact of visual reliability (blur) on network chaos and perceptual states.

Main Results:

  • The model naturally reproduces empirical effects in multisensory integration, including fluctuating unity perception and spatial beliefs.
  • Network chaos at the edge of chaos explains endogenous fluctuations and unity perception probability.
  • Increasing visual blur enhances network chaos, affecting perceptual stability.

Conclusions:

  • Intrinsic chaotic dynamics in multisensory brain networks are crucial for understanding perceptual fluctuations.
  • The model provides a neuronal mechanism for estimating unified cause probability based on network chaos.
  • The model aligns with Bayesian Causal Inference and reproduces a wide range of experimental findings.