Modeling a neural circuit for flexible control of innate behaviors

建模神经回路以灵活控制先天行为

基本信息

批准号：
9920206
负责人：
Ann Kathryn Kennedy
金额：
$ 11.08万
依托单位：
CALIFORNIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2021-02-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9920206
关键词：
Address Affect Aggressive behavior Amygdaloid structure Animal Behavior Animal Housing Animal Model Animals Area Automobile Driving Beds Behavior Behavior Control Behavioral Behavioral Model Biological Assay Brain Cell Nucleus Cells Characteristics Collection Complex Consumption Cues Data Data Analyses Dimensions Educational process of instructing Effectiveness Environment Fracture Fright Future Generations Grant Hunger Hypothalamic structure Image Image Analysis Impairment Individual Instinct Joints Laboratories Learning Link Mammals Manuals Medial Mentorship Metabolic Methods Modeling Motivation Mus Nervous System Physiology Neurons Non-linear Models Patients Pattern Phase Play Population Positioning Attribute Post-Traumatic Stress Disorders Preoptic Areas Process Property Recording of previous events Reproduction Research Retrieval Role Schizophrenia Sensory Shapes Social Behavior Social Controls Social Interaction Social status Statistical Data Interpretation Statistical Models Stress Structure Students Subgroup System Testing Time Training Work Writing addiction autism spectrum disorder base deep learning design experience experimental study flexibility generalized anxiety midbrain central gray substance multimodality neglect network models neural circuit neural model neuropsychiatric disorder neuropsychiatry novel optogenetics predictive test programs recurrent neural network relating to nervous system response skills social stressor theories tool unsupervised learning

项目摘要

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.

社会行为的适应性控制，例如侵略和繁殖，是一个关键功能神经系统。在过去的十年中，基因靶向功能的新方法在识别神经方面，对神经活动的操纵和成像已被证明是富有成果的在社会行为表达中发挥作用的亚群。但是我们对如何的理解这些神经种群在行为的动物中共同起作用。在提出的工作，我们开发了一种计算方法，以整合来自多个遗传靶向神经种群并构建了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其高可变性和复杂性，具有反抗传统基于试验的神经活动分析的特征。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社交行为和感官处理的定量和详细模型相互作用的小鼠。该行为模型将为对彻底统计分析的基础提供基础四个亚皮质核内和之间的神经动力学，它们共同跨越三个推定的处理层，第一个刚好超过感官外围，最后一个只是灰灰色灰色的前群体上游。该分析将包括1）从关节神经和行为模型中预测动物的未来行为，2）表征使用线性非线性模型对每个核中单个神经元进行行为调整，3）拟合从多个核心成像数据的网络模型，并测试了从此预测的连接性具有新型光学扰动系统的模型。最后，我们将以这些分析为基础通过下皮质核网络模型来解决行为控制的灵活性。在K99中阶段，该研究计划将使我能够在深度学习和用于数据分析的经常性神经网络：随着更多实验室的越来越重要的技能开始研究复杂的行为和中尺度的神经回路。强大的计算加州理工学院的环境，包括Pietro Perona，Markus Meister和Doris Tsao的实验室，使其成为开发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者紧密合作的独特机会测试和精炼模型。在数据演示，教学，授予写作和学生指导将使我能够过渡到独立职位。在独立R00中阶段，我将使用这些技能和剩余的目标的目标，以建立一个以实验室为目的的实验室在研究中尺度神经回路中的灵活性和行为适应性的研究中。