基于偏最小二乘的大鼠初级运动皮层解码

Decoding of rat's primary motor cortex by partial least square

为了准确解析大鼠初级运动皮层神经元发放模式并预测相应的肢体动作,实验同时记录大鼠初级运动皮层神经元峰电位发放和大鼠前肢压力,利用K均值法和主成分分析法对神经元进行分类,采用偏最小二乘分析大鼠初级运动皮层神经元活动与前肢运动参数之间的关系,并对该解码结果与维纳滤波和卡尔曼滤波算法的解码结果进行比较.实验结果表明:神经元的发放活动在压杆前0.6s开始有增加的趋势,提示大鼠的初级运动皮层神经元分布式活动可用于大鼠前肢运动的解析和预测,且偏最小二乘解码得到的预测值与真实压杆值的相关系数在0.85以上,均高于维纳滤波和卡尔曼滤波的解码结果.

In order to analytizing neurons release pattern of the primary motor cortex of rats accurately and predicting corresponding body movement, the activities of the neurons ensemble spike activities in rat＇s primary motor cortex and the forelimb pressure were recorded simultaneously in the experiment. K-means and principal component analysis were used to classification of neurons, then the partial least squares was used to analyze the relations between the neurons activities of the primary motor cortex of the rat and the forelimb motion parameters, and the results were compared with Wiener filter and Kalman filter. The experimental results indicate that the activities of neurons ensembles began a trend of increase 0. 6 second before lever pressing, Which hints the neurons distributed activities of the primary motor cortex in rats can be used to analysis and prediction its forelimb movement and the correlation coefficient between the predicted value and real pressure value is more than 0. 85 using the partial least squares, with a better decoding results than those using the Wiener filtering and Kalman filtering.