Modeling a neural circuit for flexible control of innate behaviors

对社会行为的适应性控制，如攻击性和繁殖，是一项关键功能 神经系统的一部分。在过去的十年中，基因靶向功能的新方法 神经活动的操作和成像已被证明在识别神经方面取得了惊人的成果 在社会行为表达中起作用的亚群。但我们对如何 在表现良好的动物身上，这些神经种群一起工作，仍然高度分裂。在 建议的工作，我们开发了一种计算方法来整合来自 多个基因靶向的神经群体和构建社会行为的电路模型 控制力。研究社会行为的一个重大挑战是其高度的变异性和复杂性， 这些特征违背了传统的基于试验平均的神经活动分析。要解决这个问题 挑战，我们将利用我们最近开发的自动跟踪系统来构建 自由配对的社会行为和感觉加工的定量和详细模型 相互作用的老鼠。这一行为模型将为彻底的统计分析提供基础 在四个皮质下核团集合内和之间的神经动力学，这些核团一起跨越 三个假定的加工层，第一层刚刚通过感觉外周，最后一层刚刚 中脑导水管周围灰质中运动前期人群的上游。本分析将包括1) 从联合神经和行为模型预测动物未来的行为，2)表征 用线性-非线性模型对每个核团中单个神经元的行为进行调节，以及3)拟合 对来自多个原子核的数据进行成像的网络模型，并测试由此预测的连通性 具有新的光遗传微扰系统的模型。最后，我们将在这些分析的基础上 通过皮质下核团的网络模型解决行为控制的灵活性。在K99公路上 在这一阶段，这项研究计划将使我能够发展使用深度学习和 用于数据分析的递归神经网络：随着实验室的增多，这些技能将变得越来越重要 开始研究复杂的行为和中尺度的神经电路。计算力强的 加州理工大学的环境，包括皮埃特罗·佩罗纳、马库斯·梅斯特和多丽丝·曹的实验室， 这使它成为发展我的技术培训的理想场所，而安德森实验室的实力 实验计划提供了与实验人员密切合作的独特机会 测试和改进模型。关于数据介绍、教学、赠款编写和 学生指导将使我能够过渡到一个独立的职位。在独立的R00 阶段，我将利用这些技能和我剩下的目标建立一个专注于实验室的实验室 关于中尺度神经电路的灵活性和行为适应性的研究。

The adaptive control of social behaviors, such as aggression and reproduction, is a critical function of the nervous system. In the past decade, new methods for genetically targeted functional manipulation and imaging of neural activity have proven phenomenally fruitful in identifying neural subpopulations that play a role in expression of social behaviors. But our understanding of how these neural populations work together in a behaving animal remains highly fractured. In the proposed work, we develop a computational approach to integrate neural imaging data from multiple genetically targeted neural populations and construct a circuit model of social behavior control. A significant challenge in studying social behavior is its high variability and complexity, features that defy traditional trial-averaging-based analyses of neural activity. To address this challenge, we will leverage our recently developed automated tracking system to build a quantitative and detailed model of social behaviors and sensory processing in pairs of freely interacting mice. This behavior model will provide the basis for a thorough statistical analysis of neural dynamics within and between a collection of four subcortical nuclei, which together span three putative layers of processing, the first just past the sensory periphery and the last just upstream of premotor populations in the periaqueductal gray. This analysis will include 1) predicting animals' future behavior from joint neural and behavioral models, 2) characterizing behavior tuning of individual neurons in each nucleus with a linear-nonlinear model, and 3) fitting a network model to imaging data from multiple nuclei, and testing predicted connectivity from this model with a novel optogenetic perturbation system. Finally, we will build on these analyses to address the flexibility of behavior control by network models of subcortical nuclei. In the K99 phase, this research plan will allow me to develop advanced skills in the use of deep learning and recurrent neural networks for data analysis: skills that will be increasingly important as more labs start to study complex behaviors and meso-scale neural circuits. The strong computational environment at Caltech, including the labs of Pietro Perona, Markus Meister, and Doris Tsao, makes it an ideal place to develop my technical training, while the strength of the Anderson lab's experimental program provides a unique chance to collaborate closely with experimentalists in testing and refining models. Additional training in data presentation, teaching, grant-writing, and student mentorship will allow me to transition to an independent position. In the independent R00 phase, I will use these skills and the objectives of my remaining Aims to build a laboratory focused on the study of flexibility and behavioral adaptability in meso-scale neural circuits.