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“发条”的，而是概率的。该项目将通过使用随机动态算子(SDO)为利用这一特征的新的理论和实验框架提供概念数据证明。这些新方法有可能显著提高根据大脑功能记录对动力学的预测，这反过来将在疾病过程诊断、使用刺激进行疾病控制、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。该项目汇集了一名应用数学家/神经学家和一名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领域提供跨学科的研究生和博士后培训。脑记录技术的日益复杂与同样复杂的数学方法不能完全匹配，这种方法允许对行为和神经群体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中的动态。研究中的方法避免了通常将神经分离为感觉和运动效应。他们认为神经活动代表了展开动态的概率变化。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，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”神经活动来预测动态。概念提案的证明将测试在弹道形成和反射行为扰动期间的模型预测响应，对单个棘波的实时影响的预测，以及多个神经元/群体的综合影响。在概念验证项目完成时：(1)将使用新的数据集将SDO框架与经典技术进行比较；(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。总而言之，这些数据将大大增加神经分析、神经技术和对新方法与其他方法的理解。

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.