Complementary congruent and opposite neurons achieve concurrent multisensory integration and segregation

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Cite this article as: eLife 2019;8:e43753 doi: 10.7554/eLife.43753

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Abstract

Our brain perceives the world by exploiting multisensory cues to extract information about various aspects of external stimuli. The sensory cues from the same stimulus should be integrated to improve perception, and otherwise segregated to distinguish different stimuli. In reality, however, the brain faces the challenge of recognizing stimuli without knowing in advance the sources of sensory cues. To address this challenge, we propose that the brain conducts integration and segregation concurrently with complementary neurons. Studying the inference of heading-direction via visual and vestibular cues, we develop a network model with two reciprocally connected modules modelling interacting visual-vestibular areas. In each module, there are two groups of neurons whose tunings under each sensory cue are either congruent or opposite. We show that congruent neurons implement integration, while opposite neurons compute cue disparity information for segregation, and the interplay between two groups of neurons achieves efficient multisensory information processing.

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Author details

Wen-Hao Zhang

Department of Physics, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Competing interests
The authors declare that no competing interests exist.
He Wang

Department of Physics, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2101-8683
Aihua Chen

Laboratory of Brain Functional Genomics, Primate Research Center, East China Normal University, Shanghai, China

Competing interests
The authors declare that no competing interests exist.
Yong Gu

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4437-8956
Tai Sing Lee

Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States

Competing interests
The authors declare that no competing interests exist.
K Y Michael Wong

Department of Physics, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

For correspondence
phkywong@ust.hk

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7641-5024
Si Wu

School of Electronics Engineering and Computer Science, Peking University, Beijing, China

For correspondence
siwu@pku.edu.cn

Competing interests
The authors declare that no competing interests exist.

Funding

Research Grants Council of Hong Kong (N HKUST606/12)

K Y Michael Wong

Research Grants Council of Hong Kong (605813)

K Y Michael Wong

Research Grants Council of Hong Kong (16322616)

K Y Michael Wong

Research Grants Council of Hong Kong (16306817)

K Y Michael Wong

National Basic Research Program of China (2014CB846101)

Si Wu

Natural Science Foundation of China (31261160495)

Si Wu

National Science Foundation (CISE1320651)

Tai Sing Lee

Intelligence Advanced Research Projects Activity (D16PC00007)

Tai Sing Lee

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

Peter Latham, University College London, United Kingdom

Publication history

Received: November 19, 2018
Accepted: May 22, 2019
Accepted Manuscript published: May 23, 2019 (version 1)
Version of Record published: June 13, 2019 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.