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Multisensory Bayesian Inference Depends on Synapse Maturation during Training: Theoretical Analysis and Neural

Mauro Ursino1, Cristiano Cuppini2, Elisa Magosso3

  • 1Department of Electrical, Electronic and Information Engineering University of Bologna, I 40136 Bologna, Italy mauro.ursino@unibo.it.

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Summary

This study models how the brain integrates multisensory information using a neural network. The model demonstrates Bayesian estimation and explains phenomena like the ventriloquism illusion by reweighting sensory inputs.

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Area of Science:

  • Computational Neuroscience
  • Multisensory Integration
  • Bayesian Inference

Background:

  • The brain integrates multisensory information, weighting reliable stimuli more heavily.
  • Neural mechanisms for multisensory integration and maturation are not fully understood.

Purpose of the Study:

  • To analyze audiovisual integration using a neural network model.
  • To investigate Bayesian estimation in multisensory environments.

Main Methods:

  • Developed a neural network model of audiovisual integration based on probabilistic population coding.
  • Used a Hebbian learning rule with a decay term for synapse plasticity.
  • Conducted theoretical analysis and computer simulations.

Main Results:

  • The model learned to encode unisensory likelihood functions and perform maximum likelihood estimation.
  • Cross-modal synapses encoded prior probabilities, enabling Bayesian estimation.
  • Simulations confirmed theoretical results and demonstrated deviations from perfect optimality.

Conclusions:

  • The proposed neural network model can perform Bayesian estimation in multisensory conditions.
  • The model explains the ventriloquism illusion and automatic reweighting of sensory inputs.
  • Synapse learning rules are crucial for encoding likelihoods and prior probabilities.