CRCNS: Dynamical Constraints on Neural Population Activity

CRCNS：神经群体活动的动态约束

基本信息

批准号：
9472546
负责人：
Aaron Paul Batista
金额：
$ 32万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2022-03-31
项目状态：
已结题

项目摘要

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.

认知和行为随着时间的流逝而展开。思想和行动的时间方面至少部分反映了控制它们的神经元人群的活性的时间演变。长期以来已经假设神经活动的时间过程是由基础神经回路。但是，这很难在实验上显示，因为它需要能够以各种方式扰动神经活动的能力。在这里，我们建议采用脑部计算机界面（BCI）研究神经动力学的范例。 BCI使我们能够利用动物的自愿控制将神经元种群的活性推向了我们指定的构型。这样，我们可以对动态约束进行因果测试及其与行为的关系。我们将通过在运动制备中使用的多神经元活动施加的研究动力约束猕猴的运动皮层。我们假设动态约束存在于运动皮层中，并且这些限制在运动准备过程中是形状，以驱动手臂运动。测试这些假设，我们将首先挑战动物违反推定的动态约束。然后，我们会的测试电动机制备的假设建立了适合即将到来的手臂的动态约束移动。最后，我们将使用BCI范式在运动准备期间扰动神经活动改变并唤起手臂运动。综上所述，我们拟议的工作可能会导致更丰富了解神经元网络如何产生人群动态，以及这些动态如何联系进行神经计算和行为。