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

• 批准号: 10396146
• 负责人: DEAN V BUONOMANO
• 金额: $ 37.68万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10396146
• 关键词: Address; Animals; Area; Automobile Driving; Behavior; Behavioral; Brain; Cerebellar Nuclei; Code; Computer Models; Corpus striatum structure; Cues; Data; Electrophysiology (science); Event; Food; Generations; Goals; Hippocampus (Brain); Huntington Disease; Impairment; Instruction; Interneurons; Learning; Life; Link; Medial; Modeling; Motor; Motor Cortex; Movement; Mus; Neural Network Simulation; Neurosciences; Parkinson Disease; Parvalbumins; Pattern; Phase; Play; Prefrontal Cortex; Property; Reading; Recovery; Recurrence; Rewards; Role; Shapes; Signal Transduction; Speed; Supervision; Surveys; System; Testing; Time; Work; data modeling; entorhinal cortex; experimental study; flexibility; innovation; insight; learned behavior; nervous system disorder; neural patterning; optogenetics; recurrent neural network; relating to nervous system; response

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动。 食物的供应，对生存至关重要。计算和实验工作表明， 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 和时间处理。越来越清楚的是，时间和时间预测是高度分布的 计算，然而，很少有人努力系统地对比和理解 时间在不同大脑区域编码方式之间的计算权衡。此外，虽然 汇集的证据表明，纹状体中的神经序列在时间上起着中心作用， 神经序列产生的潜在机制仍然难以捉摸。关键的是， 神经序列是在纹状体内主动产生还是由神经序列“驱动” 存在于皮质纹状体输入中。我们建议通过以下方式来解决这些主要的理解差距： 结合创新的实验和计算方法。我们的主要假设是：1） 纹状体中的神经序列提供了允许时间缩放的灵活的动态机制，即， 运动反应的加速或减慢，2）皮层输入形状神经序列的形成， 纹状体，3）局部抑制回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 某些区域，如纹状体。我们的项目是锚定在一个两个时间间隔的计时任务，其中小鼠 学习将两个线索与不同的奖励延迟联系起来，并有三个主要目标。以大规模为导向 在多个大脑区域的神经记录，我们将首先开发皮层和纹状体的递归神经网络 模型的目标是理解哪些电路图案最适合生成神经序列， 以及确定哪些模型最好地捕获实验观察到的活动模式。二是 整合神经记录和光遗传学扰动，以及计算方法， 确定纹状体中的神经序列是否由皮质输入驱动并由局部 抑制，或相反，在纹状体内积极产生。第三，我们将进行高通量 在多个大脑区域的神经活动的电生理学调查，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的潜在计算权衡。 相关性（参见说明）： 通过整合先进的计算和实验方法，这个合作项目将 提供了关于哺乳动物大脑如何能够预测外部环境的新见解。 事件将发生，使动物能够产生适当的定时运动，这在日常生活中是至关重要的。 这项工作将揭示哪些大脑回路与时间安排最密切相关，而时间安排通常在 神经系统疾病，如帕金森病和亨廷顿病。