Benyamini Miri, Zacksenhouse Miriam
Brain-computer Interfaces for Rehabilitation Laboratory, Department of Mechanical Engineering, Technion - Israel Institute of Technology Haifa, Israel.
Front Syst Neurosci. 2015 May 19;9:71. doi: 10.3389/fnsys.2015.00071. eCollection 2015.
Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.
最近对脑机接口（BMI）进行的实验表明，在开始操作接口时，尤其是在猴子停止移动其手部之后，神经调制的程度会突然增加。相比之下，与运动运动学相关的神经调制保持相对不变。在这里，我们证明在模拟BMI实验期间，由编码最优反馈控制器产生的相关信号的模拟神经元会产生类似的变化。最优反馈控制器依赖于状态估计，该估计将视觉和本体感觉反馈与来自内部模型的先验估计相结合。最优状态估计和控制所需的处理在状态空间中进行，并且通过对编码仅估计状态或还编码控制信号的两类神经元进行建模来模拟神经记录。尖峰计数作为具有线性调谐曲线的双随机泊松过程的实现而生成。该模型成功地重建了正常伸手运动期间运动学和神经活动的主要特征。最重要的是，模拟神经元的活动成功地再现了切换到脑控制时观察到的神经调制变化。进一步的理论分析和模拟表明，在正常伸手运动期间增加过程噪声会导致神经调制发生类似变化。因此，我们得出结论，在BMI实验期间观察到的神经调制变化可归因于与不完善的BMI滤波器相关的过程噪声增加，更直接地说，可归因于与状态估计和所需控制信号相关的编码信号方差的增加。
