Ades Craig, Abd Moaed A, Hutchinson Douglas T, Tognoli Emmanuelle, Du E, Wei Jianning, Engeberg Erik D
Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA.
Department of Orthopedics, University of Utah, Salt Lake City, UT 84112, USA.
Biomimetics (Basel). 2024 Jan 27;9(2):78. doi: 10.3390/biomimetics9020078.
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.
对于经历过脊髓损伤或截肢的人来说，尽管侵入性神经接口取得了显著进展，但感觉和运动控制的恢复可能并不完全。我们的目标是探索一种新型生物混合机器人手模型的可行性，以通过临床前研究平台研究触觉感知和感觉运动整合的各个方面。我们的新型生物混合模型将一只人工手与培养在多通道微电极阵列（MEA）中的生物神经网络（BNN）相结合。我们对神经活动进行解码，以控制配备触觉传感器的人工手的一根手指。指尖感觉被编码为快速适应（RA）或缓慢适应（SA）机械感受器放电模式，用于电刺激BNN。我们使用迁移学习方法，通过卷积神经网络（CNN）对MEA中传入和传出电极之间的相干性进行分类。BNN表现出对RA和SA模式进行功能特化的能力，这表现为生物混合手在触觉编码方法方面具有明显不同的机器人行为。此外，当在体外三天（DIV）的时间里为BNN提供触觉反馈时，CNN能够以97.84%±0.65%的准确率区分RA和SA编码方法。这个新型生物混合研究平台表明，BNN对触觉编码方法敏感，并且可以将机器人触觉感觉与人工手的运动控制整合起来。这为未来使用生物混合研究平台研究神经接口的各个方面、将人类风险降至最低开辟了可能性。
