CRCNS: Emergence of coordinated multi-region brain activity supporting behavior

神经元之间相互作用的变化使不同的计算和灵活的行为成为可能。是这样的 通过通过现有连接重新路由信息流，可以非常迅速地发生更改，或者更改的速度会更慢 通过更新连接。拟议的项目将研究局部和全脑动态是如何在 学习目标导向的行为。实验将使用新颖的“全光”实验技术 结合数据受限计算的蜂窝分辨率因果映射网络交互作用 模型，以前所未有的细节跟踪活着的大脑中的学习过程。调查将会进行 关注几个新的记忆引导行为任务中的学习机制，这些任务都不需要 学习，或专门为在几天内和几天内学习而量身定做的学习。这将从根本上推动 理解不同的学习机制如何塑造大脑动力学和行为。 目的1：定位局部大脑皮层神经元之间因果相互作用(有效连接)的变化 电路。建模和实验将允许解开突触可塑性的贡献和门控 学习过程中网络交互和表征的变化。 目的2：研究大脑皮层神经活动的独特特性。基于对1,000,000个神经元的细胞分辨率介观成像的初步工作导致发现空间和时间尺度 全脑动力学遵循幂定律。有趣的是，最主要的活动模式是全球性的 速度快，不同于任何现有的网络模型。拟议中的工作将揭示生物学机制。 支持在学习过程中出现这些新发现的大脑皮层状态。 目标3：研究目标1和目标2中学习的神经网络动力学的功能含义。 验证这样的假设，即这种动力学使动物能够执行灵活的记忆引导行为，工作 将重点模拟不同空间上神经活动的定向光遗传扰动的影响 根据网络动态和行为进行扩展。 总体而言，拟议的协作研究将利用私人投资促进机构的补充专门知识，以深化 了解神经动力学在不同空间尺度上的机制和功能。这个 作为该提案的一部分，开发的实验和理论方法将为全大脑提供洞察力 对记忆引导行为的控制，并将作为未来研究其他行为的路线图 由分布式大脑网络控制。

Changes in interactions between neurons enable diverse computations and flexible behaviors. Such changes can occur very rapidly by rerouting information flow through existing connections, or more slowly by updating connections. The proposed project will study how local and brain-wide dynamics arise during learning of goal-directed behaviors. Experiments will use novel ‘all-optical’ experimental techniques to causally map network interactions at cellular resolution in combination with data-constrained computational models, to follow the learning process in the living brain with unprecedented detail. The investigation will focus on learning mechanisms in several novel memory-guided behavioral tasks, that either do not require learning, or specifically tailored for studying learning within and over days. This will fundamentally advance the understanding of how different learning mechanisms shape brain-dynamics and behavior. Aim 1: Mapping changes in causal interactions (effective connectivity) between neurons in local cortical circuits. Modeling and experiments will allow disentangling contributions of synaptic plasticity and gating to changes in network interactions and representations during learning. Aim 2: Investigating unique properties of cortex-wide neural activity. Preliminary work, based on cellular-resolution mesoscopic imaging of ~1,000,000 neurons, led to the discovery that spatial and temporal scales of brain-wide dynamics follow a power-law. Intriguingly, the most dominant modes of activity are global and fast, differently from any existing network model. The proposed work will uncover biological mechanisms supporting the emergence of these newly discovered cortical states during learning. Aim 3: Investigating functional implications of learned neural network dynamics studied in Aims 1 and 2. To test the hypothesis that such dynamics enable animals to perform flexible memory-guided behaviors, work will focus on modeling the effect of targeted optogenetic perturbations of neural activity on different spatial scales on network dynamics and behavior. Overall, the proposed collaborative study will leverage the PIs complementary expertise, to deepen the understanding of mechanisms and function of neural dynamics on different spatial scales. The experimental and theoretical methods developed as part of this proposal will provide insight for brain-wide control of memory-guided behavior, and will serve as a road-map for future studies of other behaviors controlled by distributed brain networks.