Multiscale models of neural population control in spinal cord

 描述(由申请人提供)：脑记录技术的日益复杂，还无法与类似复杂的数学方法相匹配，这种方法允许对行为和神经系统深处的细胞群活动之间的关系进行建模和预测。对于发动机系统尤其如此，其主要目标是控制环境的动态。我们的目标是创建脊髓运动组件的多尺度模型，至少可以连接四个尺度：(1)单个神经元放电，(2)局部神经群体活动，(3)整个脊髓活动的地形图，以及(4)行为。我们建议使用棘化的青蛙作为我们的试验床，因为生物力学得到了很好的理解，本体感觉反馈得到了简化，脊髓可以从皮质控制中分离出来进行研究，并且在没有皮质控制的情况下可以产生可重复的复杂运动。我们将使用并进一步发展一种基于随机动态算子叠加的新的数学框架。认为神经活动引起系统动力学的改变是恰当的，这样产生的动力学(包括运动、顺应性和振荡行为)才能达到预期的结果。新的框架允许我们对动态环境的反应、顺应性控制、反射行为、单个脉冲的影响以及群体中多个神经元的综合影响进行建模。我们可以研究振荡活动(例如，在运动的中央模式生成器(CPG)中发现的)和本体感觉反馈的作用。由于这一理论在单棘波水平上运作，所有神经表征都是局部的，可以使用局部学习规则，它提供了与实际生物计算更紧密的联系，并可以深入了解脊髓用来产生复杂和不同运动的机制。为了测试我们对脊髓中间层神经元群体行为的理解，我们将(1)读出正在进行的运动的动力学，包括扰动反应和顺应性，(2)修改正在进行的运动的动力学，(3)创建显示控制功能在整个脊髓的分布的地形图。这些实验将使我们能够以一种详细的方式了解神经群体对运动动力学的控制，将神经尺度、群体尺度和运动行为尺度联系起来。这个数学框架为理解神经元群体的功能和预测它们对行为的影响提供了一个新的模型。它还提供了一个定量模型，允许预测修改纤维或损伤对行为的影响。最后，它将通过对功能性电刺激的理解，为脊髓损伤的新治疗提供基础。功能性电刺激不仅可以用来在目标肌肉中产生力，而且可以用来以身体自然使用的方式产生顺从的动力学控制。

 DESCRIPTION (provided by applicant): Increasing sophistication of brain recording technology has not been matched by a similarly sophisticated mathematical approach that permits modeling and prediction of the relation between behavior and the activity of populations of cells deep within the nervous system. This is particularly true for motor systems, where the primary goal is control of the dynamics of the environment. Our goal is to create multiscale models of motor components of the spinal cord that can link at least four scales: (1) individual neuron firing, (2) local neural population activity, (3) topographic maps of activity across the spinal cord, and (4) behavior. We propose to use the spinalized frog as our testbed, because the biomechanics are well understood, proprioceptive feedback is simplified, the cord can be studied in isolation from cortical control, and repeatable complex movements can be generated in the absence of cortical control. We will use and further develop a new mathematical framework based upon superposition of stochastic dynamic operators. It is appropriate to consider neural activity as causing a modification of the system dynamics, so that the resulting dynamics (including movement, compliance, and oscillatory behavior) achieve a desired result. The new framework allows us to model the response to dynamic environments, compliant control, reflex behavior, the effect of single spikes, and the combined effect of multiple neurons in a population. We can examine oscillatory activity (such as found in the central pattern generator (CPG) for locomotion) and the role of proprioceptive feedback. Because this theory operates at the level of single spikes and all neural representations are local and can use local learning rules, it provides a much closer link to the actual biological computations and could provide insight into the mechanisms used by the spinal cord to generate complex and varied movement. To test our understanding of the behavior of populations of neurons in the intermediate layers of spinal cord, we will (1) read out the dynamics of ongoing movement including perturbation responses and compliance, (2) modify the dynamics of ongoing movement, (3) create topographic maps showing the distribution of control functions across the cord. These experiments will allow us to understand control by neural populations of the dynamics of movement in a detailed way that links the neural scale to the population scale to the motor behavioral scale. The mathematical framework provides a new model for understanding the function of populations of neurons and predicting their effect on behavior. It also provides a quantitative model that allows the prediction of the effect of modification of firig or injury on behavior. Finally, it will provide the basis for new treatments for spinal cord injuryby giving an understanding of functional electrical stimulation that can be used not just to generate forces in target muscles, but can be used to generate smooth compliant control of dynamics in the way naturally used by the body.