PROJECT SUMMARY Manual behavior requires sensory signals from the hand, both tactile and proprioceptive, as evidenced by the severe deficits that result from somatosensory deafferentation. Three aspects of the sensory component of hand sensory function are poorly understood. First, the neural basis of touch has been studied almost exclusively with stimuli delivered passively to the skin, precluding any understanding of how tactile signals are modulated by and interact with motor commands. Second, proprioceptive signals carry information not only about the time-varying conformation of the hand, but also about manually applied forces, but proprioceptive representations of force are poorly understood. Third, stereognosis – the sense of the three-dimensional shape of objects acquired from sensory signals arising from the hand – implies the integration of tactile and proprioceptive signals, a process about which little is known. The study of active touch, hand proprioception, and stereognosis has been hindered by technical obstacles. Indeed, characterizing self-generated contact with objects has been difficult or impossible, as has tracking hand movements with sufficient precision. To overcome these obstacles, my team has developed an apparatus that allows us to measure contact events – with a sensor sheet covering the object’s surface – and track time-varying hand postures – using deep learning-based computer vision – with unprecedented precision as animals interact with objects. We then characterize the responses at every stage along the somatosensory neuraxis, from peripheral nerve through cortex. This novel experimental set up will allow us to study the neural basis of somatosensation – particularly as it relates to manual dexterity – under ecologically valid conditions. In a related line of inquiry, we leverage what we learn about sensory processing to restore the sense of touch to bionic hands. In brief, we develop algorithms to convert the output of sensors on the bionic hand into patterns of electrical stimulation of the peripheral nerve (for amputees) or of somatosensory cortex (for people with tetraplegia) to evoke meaningful tactile percepts. I am one of the principal architects of the biomimetic approach to artificial touch, which posits that encoding algorithms that mimic natural neural signals will give rise to more intuitive tactile percepts, thereby endowing bionic hands with greater dexterity. Our work on artificial touch comprises three components: evaluation of the perceptual correlates of electrical stimulation, development of sensory encoding algorithms, and assessment of the benefits of artificial touch to manual behavior. The interplay of the basic scientific results and neural engineering efforts will result in more naturalistic artificial touch for brain- controlled bionic hands.

PROJECT NARRATIVE We seek to understand the neural basis of somatosensation – touch and proprioception – during manual interactions with objects. To this end, we measure time-varying limb postures, forces exerted on sensorized objects, and neuronal responses across the entire somatosensory neuraxis, as monkeys grasp objects whose size, shape, and location is under experimenter control. We then leverage what we have learned about somatosensory coding under active conditions to develop ever more intuitive ways to convey sensory feedback to bionic hands via electrical interfaces with somatosensory cortex.