A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation.
A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation.
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DOI：
10.1088/1741-2552/abacd8
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发表时间：
2020-09-18
影响因子：
4
通讯作者：
Grill WM
中科院分区：
文献类型：
--
作者：
Kumaravelu K;Tomlinson T;Callier T;Sombeck J;Bensmaia SJ;Miller LE;Grill WM
触觉和本体感觉对运动功能是必不可少的，这从这些感觉的丧失所导致的运动缺陷中可见一斑，例如，由于感觉神经的神经病变。因此，要实现高性能的脑控假臂/手，需要恢复躯体感觉，可能是通过对躯体感觉皮质(S1)的皮质内微刺激(ICMS)。挑战是产生能唤起可理解的感知的神经元激活模式。我们提出了一个框架来设计最优的ICMS时空模式，它唤起了神经元活动的自然主义模式，并展示了优于之前四种方法的性能。我们记录了在中心外伸任务(来自Brodmann区2的本体感觉神经元)和皮肤凹陷(来自Brodmann区1的皮肤神经元)过程中S1的多单位活动。我们实现了一个皮质超柱的计算模型，并使用遗传算法设计了ICMS的时空模式(STIM)，该模式唤起了模仿实验测量的对应物的模型神经元活动模式。最后，从ICMS模式、诱发的神经元活动和产生它的刺激参数，我们训练一个递归神经网络(RNN)来学习物理刺激和仿生刺激模式之间的映射函数，即将感觉编码器集成到神经假体装置中。我们确定了激发模拟反应的ICMS模式，这些模式与电极尖端50μm内神经元的测量反应非常接近。基于RNN的感觉编码器很好地适用于未经训练的肢体运动或皮肤凹陷。使用基于模型的优化方法设计的STIM优于使用现有的线性和非线性映射设计的STIM。提出的框架产生了一个编码器，它将肢体状态或施加在假手上的压力模式转换为ICMS的时空模式，从而唤起神经元激活的自然主义模式。
Touch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g., due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge is to generate patterns of neuronal activation that evoke interpretable percepts. We present a framework to design optimal spatiotemporal patterns of ICMS that evoke naturalistic patterns of neuronal activity and demonstrate performance superior to four previous approaches. We recorded multiunit activity from S1 during a center-out reach task (from proprioceptive neurons in Brodmann’s area 2) and during application of skin indentations (from cutaneous neurons in Brodmann’s area 1). We implemented a computational model of a cortical hypercolumn and used a genetic algorithm to design spatiotemporal patterns of ICMS (STIM) that evoked patterns of model neuron activity that mimicked their experimentally-measured counterparts. Finally, from the ICMS patterns, the evoked neuronal activity, and the stimulus parameters that gave rise to it, we trained a recurrent neural network (RNN) to learn the mapping function between the physical stimulus and the biomimetic stimulation pattern, i.e., the sensory encoder to be integrated into a neuroprosthetic device. We identified ICMS patterns that evoked simulated responses that closely approximated the measured responses for neurons within 50 μm of the electrode tip. The RNN-based sensory encoder generalized well to untrained limb movements or skin indentations. STIM designed using the model-based optimization approach outperformed STIM designed using existing linear and nonlinear mappings. The proposed framework produces an encoder that converts limb state or patterns of pressure exerted onto the prosthetic hand into spatiotemporal patterns of ICMS that evoke naturalistic patterns of neuronal activation.
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影响因子：
17.1
作者：
Graczyk EL;Schiefer MA;Saal HP;Delhaye BP;Bensmaia SJ;Tyler DJ
通讯作者：
Tyler DJ
影响因子：
64.8
作者：
Ethier, C.;Oby, E. R.;Bauman, M. J.;Miller, L. E.
通讯作者：
Miller, L. E.
影响因子：
25
作者：
Dadarlat, Maria C.;O'Doherty, Joseph E.;Sabes, Philip N.
通讯作者：
Sabes, Philip N.
影响因子：
4.3
作者：
Dura-Bernal S;Li K;Neymotin SA;Francis JT;Principe JC;Lytton WW
通讯作者：
Lytton WW
影响因子：
28.1
作者：
Dyer EL;Gheshlaghi Azar M;Perich MG;Fernandes HL;Naufel S;Miller LE;Körding KP
通讯作者：
Körding KP