CRCNS: Dynamical Constraints on Neural Population Activity

认知和行为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的活动的时间演变。人们早就 假设神经活动的时间过程来自于由神经元施加的动力学约束。 潜在的神经回路然而，这很难在实验上证明，因为它需要 以各种方式精细扰乱神经活动的能力。在这里，我们建议采用脑机接口， (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，以驱动一个群体的神经元的活动，我们指定的配置。 通过这种方式，我们可以对动态约束及其与行为的关系进行因果检验。我们将 研究动态限制所施加的运动准备使用多神经元活动记录在 猕猴的运动皮层。我们假设动力学约束存在于运动皮层， 并且这些约束在电机准备期间成形以驱动臂运动。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。那就 测试运动准备为即将到来的手臂建立动力学约束的假设 运动最后，我们将使用BCI范式在运动准备期间扰乱神经活动， 改变和唤起手臂运动。总的来说，我们提出的工作可能会导致一个更丰富的 了解神经元网络如何引起种群动态，以及这些动态如何相互关联 到神经计算和行为。

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.