CRCNS: Closed-Loop Computational Neuroscience for Causally Dissecting Circuits

尽管在表征神经反应方面取得了实质性进展，但要确定 循环连接电路中的因果相互作用是由于 互联互通。这一拟议的项目开创了一个新的闭环系统计算领域 神经科学，能够在实验期间实现实时反馈刺激，以反复脱钩 并对它们之间的相互作用做出更强的因果推断。具体地说， 该项目的贡献将包括：目标1)使用现代无监督机器学习方法 拟合闭环刺激下群体反应的潜态动力系统模型。这个 开发的技术将被用来钳制遗传靶向抑制性中间神经元的放电率。 在小鼠的S 1皮质板层定位抑制细胞对感觉获得的因果影响。 兴奋性细胞。目标2)合并和扩展网络反馈控制和因果推理的工具 使用现实的实验约束来确定网络节点之间的功能连接。这些 将使用分布式技术来钳制小鼠不同S1椎板的放电率 在感官刺激过程中识别微电路层之间的功能连接的扰动。 目的3)开发一个大规模的计算建模环境，作为 社区。 意义：拟议的项目将在实验中使用刺激的事实标准改为 充分利用新录制和S.刺激技术的全部功能，以解耦当前连接的 变数和更强的因果推论。 更广泛的影响：虽然该项目使用啮齿动物体感作为模型系统，但这一结果 该项目将提供研究涉及循环回路功能障碍的神经疾病的新技术 (例如，癫痫、帕金森氏症和抑郁症)。开源实现将构成 用于闭环系统模拟实验的关键算法基础设施。该项目还将导致 在一个新兴的闭环系统计算交叉学科领域培养新学员 神经科学。 相关性(请参阅说明)： 在许多神经疾病(如癫痫、帕金森氏病和 抑郁症)很难研究，因为它们涉及复杂的反馈回路。这个项目将会发展 实时结合测量和刺激的算法，以提供强大的新工具 发现这些电路的工作原理并改变它们的运行方式。在这一领域的发现可以 有助于提高对神经性疾病的了解，并开发新的刺激疗法。

Despite substantial progress characterizing neural responses, it is particularly challenging to determine causal interactions within recurrently connected circuits due to the confounding influence of the interconnections. This proposed project pioneers a nascent field of closed-loop computational neuroscience that enables real-time feedback stimulation during experiments to decouple recurrently connected elements and make stronger causal inferences about their interactions. Specifically, the contributions of this project will include: Aim 1) Using modern unsupervised machine learning methods to fit latent state dynamical system models of population responses under closed-loop stimulation. The developed techniques will be used to clamp firing rate in genetically targeted inhibitory interneurons across S 1 cortical laminae in the mouse to map the causal effect of inhibitory cells on the sensory gain in excitatory cells. Aim 2) Merging and extending tools from network feedback control and causal inference to identify functional connections between network nodes using realistic experimental constraints. These techniques will be used to clamp firing rate in different S1 laminae of the mouse, using distributed perturbations to identify the functional connectivity between microcircuit layers during sensory stimulation. Aim 3) Developing a large-scale computational modeling environment to serve as an in si\ico testbed for the community. Significance: The proposed project changes the de facto standard use of stimulation in experiments to leverage the full power of new recording and s.timulation technology for decoupling recurrently connected variables and making stronger causal inferences. Broader impacts: While the project uses rodent somatosensation as a model system, the results of this project will provide new techniques to study neurologic disorders involving disfunction of recurrent circuits (e.g., epilepsy, Parkinson's disease and depression). The open-source implementations will constitute critical algorithmic infrastructure for closed-loop stimulation experiments. This project will also result in the production of new trainees in an emerging new interdisciplinary field of closed-loop computational neuroscience. RELEVANCE (See instructions): The neural circuits that fail in many neurologic disorders (e.g., epilepsy, Parkinson's disease and depression) are difficult to study because they involve complex feedback loops. This project will develop algorithms that combine measurements and stimulation in real-time to provide powerful new tools to uncover the operating principles of these circuits and change their operation. Discovery in this area can help improve understanding of neurologic disorders and development of new stimulation therapies.