Dynamic organization of motor cortical neural activities in learning control tasks

拟议中的研究的目的是研究适应大鼠？的运动皮层神经活动的大鼠学习执行方向控制任务的试验和错误，以获得他的控制策略。该方法是使用实验和计算建模来测试一个假设，即基于运动皮层神经活动的动态模型是在大鼠学习成功完成任务时内部开发的。当大鼠在Skinner箱中进行选择性杠杆按压时，将使用单细胞慢性皮质记录来收集神经生理学数据。 智力上的优点。拟议的研究利用一个定义明确的控制任务和电生理数据来研究感知和控制，皮层可塑性，尖峰编码/解码，以及神经皮层表示和智能行为之间的因果关系，所有这些都是在认知神经科学，神经生理学，心理学和神经工程等领域的科学辩论的中心。广泛的影响。通过这项研究获得的新知识可以帮助逆向工程强大的控制器或机器人，如脑机接口设备，以实现复杂的感觉运动整合。此外，纳米电路可以被设计为替换/提供对具有感知运动损伤的患者中的有缺陷的神经电路的补救。PI将继续努力招募女本科生和女研究生参与研究，从而为她们提供进入科学和工程前沿领域的机会。PI还将继续努力开发计算软件和使用最新的神经科学发现在课堂上使用的讲座材料。

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.