Analysis of Neural Interaction during Adaptation of Reach-to-grasp Task under Perturbation with Bayesian Networks
In this work, we took the analysis of neural interactions change in Ml of a monkey during the adaptation process for it to complete reach-to-grasp tasks with external perturbation across days. BN model was applied to model and evaluate neural interaction networks from recorded neural spike trains data of each set Our results showed that for delay period across sets, interaction level of neural network tended to be higher during later stage of adaptation than during begin stage, which indicated the monkey performed more fully preparation through adaptation. In addition, for both delay period and peri-movement period, the neural interaction networks tended to change more stably from one set to the nest as the monkey adapted to the perturbation experiment better.
Neural interaction Adaptation Perturbation Reach-to-grasp task Bayesian Networks
Dong Sang Bin Lv Huiguang He Feiyue Wang Jiping He
The State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Autom The Harrington Departemnt of Bioengineering and the Center for Neural Interface Design Arizona State
国际会议
上海
英文
623-627
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）