CRCNS: Dynamical Constraints on Neural Population Activity

认知和行为会随着时间的推移而展开。思想和行动的时间方面至少部分地反映了， 控制它们的神经元群体活动的时间演变。一直以来都是 假设神经活动的时间进程是由 潜在的神经回路。然而，这很难在实验中证明，因为它需要 以各种方式细微扰乱神经活动的能力。在这里，我们建议使用脑机接口 (BCI)范式来研究神经动力学。脑-机接口使我们能够通过利用 动物的意志控制，将一群神经元的活动驱动到我们指定的结构中。 通过这种方式，我们可以执行动态约束及其与行为的关系的因果测试。我们会 通过记录的多神经元活动来研究运动准备施加的动力学约束 猕猴的运动皮质。我们假设运动皮质中存在动力学约束， 这些约束是在运动准备过程中形成的，以驱动手臂的运动。为了测试这些 假设，我们将首先挑战动物违反假定的动力学约束。那么，我们会 测试这样的假设，即运动准备为即将到来的手臂设置了合适的动力学约束 有动静。最后，我们将使用脑-机接口范式来扰乱运动准备过程中的神经活动 改变和唤起手臂的动作。综上所述，我们提议的工作可能会带来更丰富的 了解神经元网络如何引起种群动态，以及这些动态如何相关 神经计算和行为。

Cognition and behavior unfold over time. The temporal aspects of thought and action reflect, at least in part, the temporal evolution of the activity of the populations of neurons that control them. It has long been hypothesized that the time course of neural activity arises from dynamical constraints imposed by the underlying neural circuitry. However, this has been difficult to show experimentally because it requires the ability to finely perturb neural activity in varied ways. Here, we propose to employ a brain-computer interface (BCI) paradigm to study neural dynamics. A BCI enables us to perturb neural activity by harnessing the animal's volitional control to drive the activity of a population of neurons into configurations that we specify. In this way, we can perform causal tests of dynamical constraints and their relation to behavior. We will study dynamical constraints imposed by motor preparation using multi-neuronal activity recorded in the motor cortex of macaque monkeys. We hypothesize that dynamical constraints exist in the motor cortex, and that these constraints are shaped during motor preparation to drive arm movements. To test these hypotheses, we will first challenge the animals to violate the putative dynamical constraints. Then, we will test the hypothesis that motor preparation sets up dynamical constraints appropriate for the upcoming arm movement. Finally, we will use the BCI paradigm to perturb neural activity during movement preparation to alter and evoke arm movements. Taken together, our proposed work will likely lead to a richer understanding of how networks of neurons give rise to population dynamics, and how those dynamics relate to neural computation and behavior.