Dynamic organization of motor cortical neural activities in learning control tasks

本研究的目的是研究大鼠的适应性。当大鼠学习执行方向控制任务时，通过试错来获得控制策略，S运动皮质神经活动。该方法是使用实验和计算模型来验证一个假设，即在大鼠学习成功完成任务时，内部形成了一个基于运动皮质神经活动的动态模型。当大鼠在斯金纳箱中执行选择性杠杆按压时，将利用单细胞慢性皮层记录来收集神经生理学数据。知识价值。本研究利用明确定义的控制任务和电生理学数据来研究感知和控制的整合、皮层可塑性、脉冲编码/解码以及神经皮层表征与智能行为之间的因果关系，所有这些都是认知神经科学、神经生理学、心理学和神经工程学等领域科学辩论的中心。广泛的影响。通过本研究获得的新知识可以帮助逆向工程鲁棒控制器或机器人，如脑机接口设备，以实现复杂的感觉运动集成。此外，纳米电路可能被设计用于替代/提供治疗知觉运动损伤患者的神经回路缺陷。PI将继续努力招收女本科生和研究生参与研究，从而使她们能够进入科学和工程的前沿领域。PI还将继续努力开发计算软件和使用最新神经科学发现的课堂教学材料。

The objective of the proposed research is to examine adaptation in rat?s motor cortical neural activities as the rat learns to perform a directional control task by trial and error to derive his control strategy. The approach is to use experimental and computational modeling to test a hypothesis that a dynamic model based on motor cortical neural activities is developed internally as the rat learns to succeed in the task. Single cell chronic cortical recordings will be utilized to collect neurophysiological data as the rat performs his selective lever pressing in a Skinner box. Intellectual merit. The proposed research makes use of a well defined control task and electrophysiological data to examine the integration of perception and control, cortical plasticity, spike coding/decoding, and the causal relationship between neural cortical representation and intelligent behaviors, all of which are at the center of scientific debate in areas such as cognitive neuroscience, neurophysiology, psychology, and neuroengineering. Broad impact. New knowledge gained through this study can help reverse engineer robust controllers or robots such as brain machine interface devices to for sophisticated sensory motor integration. Additionally, nano-circuits may be designed to replace/provide remedy for deficient neural circuits in patients with perceptual-motor impairment. The PI will continue efforts in recruiting female undergraduate and graduate students to participate in the research and thus providing them access to frontier fields of science and engineering. The PI will also continue efforts in developing computational software and lecture materials using latest neuroscientific findings for use in the classroom.