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， 因果关系的相互作用内循环连接的电路由于混淆的影响， 相互联系该项目开创了闭环计算的新兴领域， 神经科学，使实时反馈刺激在实验过程中解耦循环 连接的元素，并对它们的相互作用做出更强的因果推断。具体而言是 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， 拟合闭环刺激下种群反应的潜态动力系统模型。的 开发的技术将用于钳制遗传靶向抑制性中间神经元的放电率 在小鼠的S1皮质层上，绘制抑制细胞对感觉获得的因果作用， 兴奋细胞目标2）融合和扩展网络反馈控制和因果推理工具 使用现实的实验约束来识别网络节点之间的功能连接。这些 技术将用于钳制小鼠的不同S1板中的放电率，使用分布的 微电路层之间的功能连接性的识别。 目标3）开发一个大规模计算建模环境，作为以下方面的现场试验平台： 社会各界 意义：拟议的项目改变了实验中刺激的实际标准使用， 充分利用新的录音和S.刺激技术的全部功能， 变量和做出更强的因果推断。 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，但其结果是， 该项目将提供新的技术来研究涉及复发性回路功能障碍的神经系统疾病 (e.g.,癫痫、帕金森病和抑郁症）。开源实现将构成 闭环刺激实验的关键算法基础设施。该项目还将导致 在一个新兴的新的跨学科领域的闭环计算生产新的学员 神经科学 相关性（参见说明）： 在许多神经系统疾病中失败的神经回路（例如，癫痫、帕金森病和 抑郁症）很难研究，因为它们涉及复杂的反馈回路。该项目将开发 联合收割机实时结合测量和刺激的算法，提供强大的新工具， 揭示这些电路的工作原理并改变它们的操作。在这个领域的发现可以 有助于提高对神经系统疾病的理解和开发新的刺激疗法。

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.