Bioinspired multisensory neural network with crossmodal integration and recognition

Hongwei Tan (), Yifan Zhou, Quanzheng Tao, Johanna Rosen and Sebastiaan van Dijken ()
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Hongwei Tan: Aalto University School of Science
Yifan Zhou: Aalto University School of Science
Quanzheng Tao: Linköping University
Johanna Rosen: Linköping University
Sebastiaan van Dijken: Aalto University School of Science

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract The integration and interaction of vision, touch, hearing, smell, and taste in the human multisensory neural network facilitate high-level cognitive functionalities, such as crossmodal integration, recognition, and imagination for accurate evaluation and comprehensive understanding of the multimodal world. Here, we report a bioinspired multisensory neural network that integrates artificial optic, afferent, auditory, and simulated olfactory and gustatory sensory nerves. With distributed multiple sensors and biomimetic hierarchical architectures, our system can not only sense, process, and memorize multimodal information, but also fuse multisensory data at hardware and software level. Using crossmodal learning, the system is capable of crossmodally recognizing and imagining multimodal information, such as visualizing alphabet letters upon handwritten input, recognizing multimodal visual/smell/taste information or imagining a never-seen picture when hearing its description. Our multisensory neural network provides a promising approach towards robotic sensing and perception.

Date: 2021
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DOI: 10.1038/s41467-021-21404-z

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