A physical emulation of somatosensory cortex as a Neuromorphic Twin for neural prostheses

Abstract Loss of tactile feedback in patients with paralysis or limb loss prevents the integration of sensory-motor signals, limiting dexterous motor control. Recent advances in the neurostimulation of the human somatosensory cortex offer the possibility of restoring touch in these patients. Brain-computer interfaces and bidirectional prostheses with implantable electrodes can restore feedback, improving control, awareness, and quality of life. However, current electrical stimulation methods lack the fidelity to consistently and reliably reproduce natural sensations, and the impact of this stimulation on the somatosensory cortex remains unclear. Here we propose to use a mixed-signal neuromorphic processing system to construct a “neuromorphic twin” of the somatosensory cortex, following a bottom-up approach for emulating and reproducing its intricate dynamics. We use the analog properties of electronic neuron and synapse circuits to faithfully replicate neural and synaptic response properties. To reproduce effects at the network level, we configure the neuromorphic processor to implement the recurrent connectivity patterns of the somatosensory cortex among different populations of excitatory and inhibitory silicon neurons. Our findings demonstrate the twin’s ability to reproduce the spatiotemporal firing patterns of the native neuronal population receiving afferent fiber inputs, resulting in a highly bio-realistic model that can be used to investigate the effect of different stimulation patterns. This physical emulation of the cortical circuits represents therefore an additional tool to understand and predict the somatosensory cortex behavior under neurostimulation. Using this system, neurostimulation patterns can be optimized to produce more natural sensations and ultimately improve the quality of restored feedback, leading to a better quality of life for affected patients.