Synaptic integration across first-order tactile neurons can discriminate edge orientations with high acuity and speed

Our ability to manipulate objects relies on tactile inputs signaled by first-order tactile neurons that innervate the glabrous skin of the hand. A fundamental feature of these first-order tactile neurons is that their distal axon branches in the skin and innervates many mechanoreceptive end organs, yielding spatially-complex receptive fields with several highly sensitive zones. Recent work indicates that this peripheral arrangement constitutes a neural mechanism enabling first-order tactile neurons to signal high-level geometric features of touched objects, such as the orientation of an edge moving across the skin. Here we show how second-order tactile neurons could integrate these complex peripheral signals to compute edge-orientation. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. Importantly, our models suggest that first-order tactile neurons innervate mechanoreceptors in the fingertips following a random sampling scheme. We then use the model neurons as the basis for a simulation of the peripheral neuronal population and its synaptic integration by second-order tactile neurons (i.e. in the spinal cord and brainstem). Our model networks indicate that computations done by second-order neurons could underlie the human ability to process edge orientations with high acuity and speed. In particular, our models suggest that synaptic integration of AMPA inputs within short timescales are critical for discriminating fine orientations, whereas NMDA-like synapses refine discrimination and maintain robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the tactile processing pathway and how they may be critical for supporting hand function.