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.
Highlights
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
Each photomemristor works as an artificial optoelectronic (OE) synapse that receives signals from a sensory nerve and produces a post-synaptic current (PSC) at the optical spiking rate a Hearing Vision Smell Taste
The demonstrations are simple compared to biological systems, the hierarchical architectures, principle concepts, and cognitive functionalities of our multisensory neural network (MSeNN) system allow for straightforward extensions to other sensory integrations, providing a promising strategy toward robotic sensing and cognition
Summary
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. We present a bioinspired spiking multisensory neural network (MSeNN) that integrates artificial vision, touch, hearing, and simulated smell and taste senses with crossmodal learning via artificial neural networks (ANNs). The hierarchical and cognitive MSeNN is capable of sensing, encoding, transmitting, decoding, filtering, memorizing, and recognizing multimodal information, but it enables crossmodal recognition and imagination through crossmodal learning for robotic sensing and processing
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