CRCNS: Collaborative Research: Probabilistic Representation of Dynamic Action and Superposition in Spinal Cord Neural Populations - Advancing Theory and Experiment

CRCNS：协作研究：脊髓神经群体动态作用和叠加的概率表示 - 推进理论和实验

• 批准号: 1514622
• 负责人: Terence Sanger
• 金额: $ 20万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514622&HistoricalAwards=false
• 关键词: CRCNS; Collaborative; Research; Probabilistic; Representation

The operation of the brain is not 'clockwork', but rather probabilistic. The project will provide proof of concept data for a new theoretical and experimental framework that utilizes this feature, by using stochastic dynamic operators (SDOs). These new methods have potential to significantly improve predictions of dynamics from recordings of brain function, which in turn would have significant technological and medical impacts in areas including disease process diagnosis, disease control using stimulation, robot prostheses and brain machine interface designs, neural prostheses, and neurally-driven augmentation or replacement. The project brings together a collaboration between an applied mathematician/neurologist and a comparative neurophysiologist, and will provide interdisciplinary graduate and postdoctoral training at the cutting edge of neuroscience, stochastic methods and control. Increasing sophistication of brain recording technology is not fully matched by an equally sophisticated mathematical approach that permits modeling and direct prediction of the relation between behavior and the activity of neural populations. For motor systems, the primary goal is control of dynamics in the environment. The methods under investigation avoid the usual neural separation into sensory and motor effects. They treat neural activity as representing probabilistic alterations of unfolding dynamics. More specifically, the SDO framework considers neural activity as causing a modification of the overall system dynamics, so that the resulting dynamics (including movement, compliance, and oscillatory behavior) achieve a desired result. This allows principled engineering solutions and use of 'big' neural activity to predict dynamics. The proof of concept proposal will test model prediction responses during trajectory formation and perturbation in reflex behavior, prediction of real-time effect of single spikes, and combined effect of multiple neurons/populations. On proof of concept project completion: (1) The SDO framework will be compared with classical techniques using novel data sets; (2) Basic feasibility of real-time robot control from spinal neural activity in a model system will be assessed. Together, these data will all add significantly to neural analysis, neurotechnology and understanding of the novel methods in relation to others.

大脑的运作不是“发条”的，而是概率性的。该项目将通过使用随机动态算子 (SDO) 为利用此功能的新理论和实验框架提供概念验证数据。这些新方法有可能显着改善对大脑功能记录的动态预测，这反过来又会对疾病过程诊断、利用刺激进行疾病控制、机器人假肢和脑机接口设计、神经假肢以及神经驱动的增强或替换等领域产生重大的技术和医学影响。 该项目汇集了应用数学家/神经学家和比较神经生理学家之间的合作，并将提供神经科学、随机方法和控制前沿的跨学科研究生和博士后培训。日益复杂的大脑记录技术并不能完全与同样复杂的数学方法相匹配，这种数学方法允许对神经群体的行为和活动之间的关系进行建模和直接预测。对于电机系统，主要目标是控制环境动态。正在研究的方法避免了通常的神经分离为感觉和运动效应。他们将神经活动视为代表展开动力学的概率变化。更具体地说，SDO 框架认为神经活动会导致整个系统动力学的修改，以便所产生的动力学（包括运动、顺应性和振荡行为）达到预期的结果。这使得有原则的工程解决方案和使用“大”神经活动来预测动力学成为可能。概念验证提案将测试轨迹形成期间的模型预测响应和反射行为的扰动、单个尖峰的实时效果的预测以及多个神经元/群体的组合效果。在概念验证项目完成时： (1) SDO 框架将与使用新颖数据集的经典技术进行比较； (2) 将评估模型系统中通过脊髓神经活动进行实时机器人控制的基本可行性。总之，这些数据将显着增强神经分析、神经技术以及对其他新方法的理解。