Modeling a neural circuit for flexible control of innate behaviors

社会行为的适应性控制，如攻击和繁殖，是一个关键的功能， 的神经系统。在过去的十年中，新的方法，基因靶向功能 神经活动的操纵和成像已经证明在识别神经活动方面非常富有成效。 在社会行为表达中起作用的亚群。但我们对 这些神经群在一个行为动物中一起工作仍然是高度断裂的。在 建议的工作，我们开发了一种计算方法，将神经成像数据从 多个遗传目标神经群体，并构建社会行为的电路模型 控制研究社会行为的一个重大挑战是它的高度可变性和复杂性， 这些特征挑战了传统的基于试验平均的神经活动分析。为了解决这个 挑战，我们将利用我们最近开发的自动跟踪系统， 定量和详细的模型的社会行为和感官处理对自由 相互作用的老鼠该行为模型将为以下方面的全面统计分析提供基础： 四个皮质下核团的集合内部和之间的神经动力学，它们共同跨越 三个假定的处理层，第一个刚刚过去的感觉周边和最后一个刚刚 位于中脑导水管周围灰质的前运动细胞群上游。该分析将包括1） 从联合神经和行为模型预测动物的未来行为，2）表征 用线性-非线性模型对每个核中的单个神经元进行行为调谐，以及3）拟合 一个网络模型，从多个核成像数据，并测试预测的连接，从这个 模型与一种新的光遗传学微扰系统。最后，我们将根据这些分析， 通过皮层下核团的网络模型来解决行为控制的灵活性。在K99 在这个阶段，这个研究计划将使我能够发展使用深度学习的高级技能， 用于数据分析的递归神经网络：随着越来越多的实验室， 开始研究复杂行为和中尺度神经回路。强大的计算 包括Pietro Perona、Markus Meister和Doris Tsao的实验室， 这使它成为一个理想的地方，发展我的技术培训，而安德森实验室的力量， 实验计划提供了一个独特的机会，与实验学家密切合作， 测试和改进模型。在数据展示、教学、赠款撰写和 学生指导将使我能够过渡到一个独立的位置。在独立的R 00 阶段，我将利用这些技能和我剩余的目标，建立一个实验室的重点 研究中尺度神经回路的灵活性和行为适应性。

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.