How the prior information shapes neural networks for optimal multisensory integration

He Wang, Wen Hao Zhang, K. Y.Michael Wong, Si Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Extensive studies suggest that the brain integrates multisensory signals in a Bayesian optimal way. In this work, we consider how the couplings in a neural network model are shaped by the prior information when it performs optimal multisensory integration and encodes the whole profile of the posterior. To process stimuli of two modalities, a biologically plausible neural network model consists of two modules, one for each modality, and crosstalks between the two modules are carried out through feedforward cross-links and reciprocal connections. We found that the reciprocal couplings are crucial to optimal multisensory integration in that their pattern is shaped by the correlation in the joint prior distribution of sensory stimuli. Our results show that a decentralized architecture based on reciprocal connections is able to accommodate complex correlation structures across modalities and utilize this prior information in optimal multisensory integration.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Networks - ISNN 2017 - 14th International Symposium, ISNN 2017, Proceedings
EditorsFengyu Cong, Qinglai Wei, Andrew Leung
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319590806
StatePublished - 2017
Externally publishedYes
Event14th International Symposium on Neural Networks, ISNN 2017 - Sapporo, Hakodate, and Muroran, Hokkaido, Japan
Duration: Jun 21 2017Jun 26 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10262 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Symposium on Neural Networks, ISNN 2017
CitySapporo, Hakodate, and Muroran, Hokkaido


  • Bayesian inference
  • Multisensory processing
  • Recurrent neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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