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

大脑的运作不是“钟表”，而是概率。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数据证明。这些新方法有可能显着改善从大脑功能记录中预测动态的能力，这反过来又会在疾病过程诊断、使用刺激控制疾病、机器人假体和脑机接口设计、神经假体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，并将在神经科学，随机方法和控制的前沿提供跨学科的研究生和博士后培训。越来越复杂的大脑记录技术并不完全匹配一个同样复杂的数学方法，允许建模和直接预测行为和神经群体的活动之间的关系。对于运动系统，主要目标是控制环境中的动力学。研究中的方法避免了通常的神经分离成感觉和运动效应。他们将神经活动视为展现动力学的概率性改变。更具体地说，SDO框架将神经活动视为导致整个系统动力学的修改，使得所产生的动力学（包括运动、顺应性和振荡行为）实现期望的结果。这允许有原则的工程解决方案和使用“大”神经活动来预测动态。概念证明提案将测试轨迹形成和反射行为扰动期间的模型预测响应，预测单个尖峰的实时效果以及多个神经元/群体的组合效果。在概念验证项目完成时：（1）SDO框架将与使用新数据集的经典技术进行比较;（2）将评估模型系统中脊髓神经活动的实时机器人控制的基本可行性。总之，这些数据都将大大增加神经分析，神经技术和理解与其他相关的新方法。

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.