Department of Biomedical Engineering, Duke University, Durham, NC, United States of America.
Deptartment of Physiology, Northwestern University, Chicago, IL, United States of America.
J Neural Eng. 2020 Sep 18;17(4):046045. doi: 10.1088/1741-2552/abacd8.
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 (STIM) 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 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 STIM that evoke naturalistic patterns of neuronal activation.
触觉和本体感觉对于运动功能至关重要，例如由于感觉神经的神经病变而导致这些感觉丧失，会导致运动功能障碍。因此，要实现高性能的脑控假肢/手，就需要恢复体感，也许可以通过皮层内微刺激（ICMS）来刺激体感皮层（S1）。挑战在于产生可引发可解释感知的神经元激活模式。我们提出了一种设计最佳时空 ICMS（STIM）模式的框架，该模式可引发自然的神经元活动模式，并证明优于以前的四种方法。
我们在中心向外伸手任务期间（来自布罗德曼区域 2 的本体感受神经元）以及皮肤凹陷期间（来自布罗德曼区域 1 的皮肤神经元）从 S1 记录多单位活动。我们实现了皮层超柱的计算模型，并使用遗传算法设计了 STIM，该 STIM 引发了模型神经元活动模式，模仿了他们通过实验测量得到的模式。最后，从 ICMS 模式、诱发的神经元活动以及产生这些活动的刺激参数中，我们训练了一个递归神经网络（RNN）来学习物理刺激和仿生刺激模式之间的映射函数，即要集成到神经假肢设备中的感觉编码器。
我们确定了 ICMS 模式，这些模式引发的模拟响应非常接近电极尖端附近 50 µm 内神经元的测量响应。基于 RNN 的感觉编码器对未经训练的肢体运动或皮肤凹陷具有很好的泛化能力。使用基于模型的优化方法设计的 STIM 优于使用现有线性和非线性映射设计的 STIM。
所提出的框架产生了一个编码器，该编码器将肢体状态或施加到手假肢上的压力模式转换为引发自然的神经元激活模式的 STIM。
