Emulating sensation by bridging neuromorphic computing and multisensory integration

Multisensory perception produces vast amounts of data requiring efficient processing. This paper focuses on the multisensory example of touch in biological and artificial systems. We integrate philosophical theories of multisensory perception with neuromorphic hardware and demonstrate how classical sensory integration concepts can enhance artificial sensory systems. This approach bridges theoretical neuroscience and computational applications using philosophical tools. We contrast human touch perception, involving feature binding, with artificial perception in neuromorphic computing, where such integration is absent. Two theoretical frameworks are of interest, feature binding and modalities as conventional kinds, in evaluating their relevance to artificial touch. Our findings suggest that a hardware-tailored adaptation of the conventional modalities approach accurately reflects artificial touch perception. Unlike human perception, artificial systems process sensory data separately, lacking binding mechanisms. We explore the implications of these differences, highlighting challenges in replicating human sensory experiences and the role of subjective experience in perception.