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

预测外部事件何时发生的能力，例如预测捕食者或 食物的可获得性是生存的关键。计算和实验工作的融合表明 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散的 然而，计算几乎没有努力系统地对比和理解 时间在不同大脑区域的编码方式之间的计算权衡。此外，虽然 越来越多的证据表明，纹状体中的神经序列在计时中起着核心作用 神经序列产生的潜在机制仍然难以捉摸。关键的是，目前尚不清楚 神经序列是在纹状体内活跃产生的，还是由神经序列“驱动”的 存在于皮质纹状体输入。我们建议通过一个 创新的实验和计算方法的结合。我们的关键假设是：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)皮质输入形成神经序列 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。我们的项目以两个间隔的计时任务为基础，在这个任务中，老鼠 学会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。以大范围为导向 多个脑区的神经记录我们将首先开发皮质和纹状体递归神经网络 以了解哪些电路主题最适合生成神经序列为目标的模型， 并确定哪种模型最能捕捉到实验中观察到的活动模式。第二，我们将 将神经记录和光遗传扰动与计算方法相结合，以 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 抑制性，或者相反，在纹状体内活跃地产生。第三，我们将开展高通量 对多个脑区的神经活动进行电生理学调查，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(请参阅说明)： 通过整合先进的计算和实验方法，这个合作项目将 提供关于哺乳动物大脑如何能够预测何时外部环境的全新见解 活动将会发生，使动物能够产生在日常生活中至关重要的适当时机的动作。 这项工作将揭示哪些大脑回路与计时关系最密切，而计时通常在 帕金森氏症和亨廷顿氏症等神经系统疾病。

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.