CRCNS: Multiple clocks for the encoding of time in corticostriatal circuits

CRCNS：皮质纹状体电路中时间编码的多个时钟

基本信息

批准号：
10697316
负责人：
DEAN V BUONOMANO
金额：
$ 37.96万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-23 至 2026-08-31
项目状态：
未结题

项目摘要

The ability to predict when external events will occur, such as anticipating the actions of a predator or the availability of food, is critical for survival. Converging computational and experimental work suggests that dynamically changing patterns of neural activity, including neural sequences, underlie temporal prediction and temporal processing. It is increasingly clear that timing and temporal prediction are highly distributed computations, however, there has been little effort to systematically contrast and understand the computational tradeoffs between how time is encoded in different brain areas. Furthermore, while converging evidence suggests neural sequences in the striatum play a central role in timing, the mechanisms underlying the generation of neural sequences remains elusive. Critically, it is not known whether neural sequences are actively generated within the striatum or are “driven” by neural sequences present in corticostriatal inputs. We propose to address these major gaps in understanding with a combination of innovative experimental and computational approaches. Our key hypotheses are that: 1) neural sequences in the striatum provide a flexible dynamical regime that allows for temporal scaling, i.e., speeding-up or slowing-down of motor responses, 2) cortical input shapes neural sequence formation in the striatum, 3) local inhibitory circuits serve to refine the quality of these sequences in the striatum, and 4) neural dynamics encoding time are widely distributed throughout the brain but are more accurate in certain areas such as the striatum. Our project is anchored in a two-interval timing task in which mice learn to associate two cues with different reward delays, and has three major aims. Guided by large-scale neural recordings in multiple brain areas we will first develop cortical and striatal recurrent neural network models with the goal of understanding which circuit motifs are best suited to generate neural sequences, and determining which models best capture the experimentally observed activity patterns. Second, we will integrate neural recordings and optogenetic perturbations, together with computational approaches, to determine whether neural sequences in the striatum are driven by cortical input and refined by local inhibition, or in contrast actively generated within the striatum. Third, we will carry out a high-throughput electrophysiological survey of neural activity in multiple brain areas, to identify which areas contain the most accurate temporal codes as well as the potential computational tradeoffs between different codes. RELEVANCE (See instructions): By integrating advanced computational and experimental approaches, this collaborative project will provide fundamentally new insights about how the mammalian brain is able to predict when external events will occur, enabling animals to produce appropriately timed movements that are critical in daily life. This work will reveal which brain circuits are most strongly implicated in timing, which is often impaired in neurological disorders such as Parkinson’s and Huntington’s disease.

预测外部事件何时发生的能力，例如预测捕食者或动物的行为计算和实验工作表明，食物的供应对于生存至关重要。动态变化的神经活动模式（包括神经序列）是时间预测的基础越来越明显的是，时间和时间预测是高度分布的。然而，很少有人努力系统地对比和理解此外，不同大脑区域的时间编码方式之间的计算权衡。汇聚的证据表明纹状体中的神经序列在时间安排中发挥着核心作用，重要的是，神经序列生成的机制仍然是未知的。神经序列是在纹状体内主动生成还是由神经序列“驱动” 我们建议通过以下方式解决这些主要的理解差距。我们的主要假设是：1）创新的实验和计算方法的结合。纹状体中的神经序列提供了灵活的动态机制，允许时间缩放，即运动反应的加速或减慢，2）皮质输入塑造神经序列的形成纹状体，3) 局部抑制电路用于改善纹状体中这些序列的质量，以及 4）神经动力学编码时间广泛分布在整个大脑中，但在以下方面更准确我们的项目以小鼠的两个间隔计时任务为基础。学习将两种线索与不同的奖励延迟联系起来，并具有三个主要目标。多个大脑区域的神经记录，我们将首先开发皮质和纹状体循环神经网络模型的目的是了解哪些电路图案最适合生成神经序列，其次，我们将确定哪些模型最能捕捉实验观察到的活动模式。将神经记录和光遗传学扰动与计算方法结合起来确定纹状体中的神经序列是否由皮质输入驱动并由局部细化抑制，或者相反在纹状体内积极产生第三，我们将进行高通量。对多个大脑区域的神经活动进行电生理调查，以确定哪些区域包含最准确的时间代码以及不同代码之间潜在的计算权衡。相关性（参见说明）：通过整合先进的计算和实验方法，该合作项目将提供关于哺乳动物大脑如何能够预测外部环境的全新见解事件将会发生，使动物能够产生在日常生活中至关重要的适当定时的运动。这项工作将揭示哪些大脑回路与时序关系最密切，而时序经常受到损害。神经系统疾病，如帕金森病和亨廷顿舞蹈病。