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

Ades, Craig; Moaed, A.; Du, E; Wei, Jianning; Tognoli, Emmanuelle; Engeberg, Erik D.

doi:10.1109/haptics52432.2022.9765605

Cited by 1 publication

(1 citation statement)

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“…The evoked neuronal activity recorded from the efferent electrode (Figure 1a, black) is decoded to control the robotic hand (Figure 1b-d). We have recently shown that this biohybrid hand can respond to different tactile sensations [36]. New in this work, we delve into the impact that different system embodiment configurations and tactile sensation encoding models have upon the neuronal dynamics of the biohybrid hand.…”

Section: Introductionmentioning

confidence: 95%

Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration

Ades,

Abd,

Hutchinson

et al. 2024

Biomimetics

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For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.

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Section: Introductionmentioning

confidence: 95%