Shoham Shy, Paninski Liam M, Fellows Matthew R, Hatsopoulos Nicholas G, Donoghue John P, Normann Richard A
Faculty of Biomedical Engineering, the Technion, Israel Institute of Technology, Haifa 32000, Israel.
IEEE Trans Biomed Eng. 2005 Jul;52(7):1312-22. doi: 10.1109/TBME.2005.847542.
A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.
多项对运动系统的研究表明，大多数初级运动皮层神经元在其时变活动模式中代表与简单运动相关的运动学和动力学量。这种编码关系的一个例子是 firing rate 相对于手部运动方向的余弦调谐。我们使用二维（2-D）连续追踪任务期间的多电极阵列记录，对与运动相关的运动神经元的统计编码模型进行了系统开发。我们的方法通过利用二维归一化占用图、级联线性 - 非线性（LN）系统模型以及一种描述离散随机系统变异性的方法，避免了对响应进行大量平均。我们发现，大多数与运动相关的运动神经元的预期 firing rate 通过线性变换与运动学值相关，约 1/3 的神经元存在显著的非线性失真。在许多神经元中，神经反应的测量变异性明显非泊松分布，并且在此定义和引入的“归一化高斯”统计模型能很好地捕捉这种变异性。该统计模型无缝集成到基于序贯蒙特卡罗滤波器从神经反应中解码运动的近乎最优递归方法中。
