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Robotically Embodied Biological Neural Networks to Investigate Haptic Restoration with Neuroprosthetic Hands

Neuroprosthetic limbs reconnect severed neural pathways for control of (and increasingly sensation from) an artificial limb. However, the plastic interaction between robotic and biological components is poorly understood. To gain such insight, we developed a novel noninvasive neuroprosthetic research platform that enables bidirectional electrical communications (action, sensory perception) between a dexterous artificial hand and neuronal cultures living in a multichannel microelectrode array (MEA) chamber. Artificial tactile sensations from robotic fingertips were encoded to mimic slowly adapting (SA) or rapidly adapting (RA) mechanoreceptors. Afferent spike trains were used to stimulate neurons in a region of the neuronal culture. Electrical activity from neurons at another region in the MEA chamber was used as the motor control signal for the artificial hand. Results from artificial neural networks (ANNs) showed that the haptic model used to encode RA or SA fingertip sensations affected biological neural network (BNN) activity patterns, which in turn impacted the behavior of the artificial hand. That is, the exhibited finger tapping behavior of this closed-loop neurorobotic system showed statistical significance (p<0.01) between the haptic encoding methods across two different neuronal cultures and over multiple days. These findings suggest that our noninvasive neuroprosthetic research platform can be used to devise high-throughput experiments exploring how neural plasticity is affected by the mutual interactions between perception and action.