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Development of a Bayesian Estimator for Audio-Visual Integration: A Neurocomputational Study.

Mauro Ursino1, Andrea Crisafulli2, Giuseppe di Pellegrino2

  • 1Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.

Frontiers in Computational Neuroscience
|October 20, 2017
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Summary

This study enhances a neural network model for audio-visual integration, showing it can learn Bayesian estimation principles from sensory data. The improved model accurately simulates how the brain combines senses, including visual biases and the ventriloquism effect.

Keywords:
multisensory integrationneural networksperception biasprior probabilityventriloquism

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Sensory Processing

Background:

  • The brain integrates multisensory information for coherent perception, often following Bayesian principles.
  • Neural mechanisms and developmental aspects of multisensory integration remain incompletely understood.
  • Previous models explored audio-visual integration but lacked realistic visual stimulus distributions.

Purpose of the Study:

  • To refine a neural network model of audio-visual integration with more realistic visual stimulus properties.
  • To investigate how Bayesian estimation principles emerge from sensory statistics in a neural network.
  • To simulate and understand the neural basis of multisensory integration behaviors like visual bias and ventriloquism.

Main Methods:

  • Developed a topologically organized neural network with auditory and visual unimodal areas.
  • Incorporated realistic visual stimulus distributions with varying spatial accuracy and prior probabilities.
  • Trained synaptic connections using Hebbian potentiation and a decay term based on sensory experience.

Main Results:

  • Trained neurons adapted receptive fields to match input accuracy, reflecting likelihood estimation.
  • Visual neurons' preferred positions shifted centrally, encoding prior visual probabilities.
  • Cross-modal synapses encoded audio-visual co-occurrence priors, simulating Bayesian estimator properties and reproducing behavioral data, including unisensory visual bias and cross-modal ventriloquism effects.

Conclusions:

  • The enhanced neural network model successfully simulates key aspects of Bayesian multisensory integration.
  • The model demonstrates how realistic sensory statistics can lead to the development of complex perceptual behaviors.
  • This work provides insights into the neural mechanisms underlying audio-visual perception and spatial estimation.