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：
10576922
负责人：
Ann Kathryn Kennedy
金额：
$ 24.9万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-15 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10576922
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 Collaborations 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 Motor Mus Nervous System Physiology Neurons Non-linear Models Patients Pattern Phase 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 deep learning design experience experimental study flexibility generalized anxiety laboratory experiment midbrain central gray substance multimodality neglect network models neural neural circuit neural model neuroimaging neuropsychiatric disorder neuropsychiatry novel optogenetics programs recurrent neural network 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（例如攻击和繁殖）的适应性控制是一项关键功能神经系统的。在过去的十年中，基因靶向功能的新方法神经活动的操纵和成像已被证明在识别神经元方面取得了惊人的成果在社会行为表达中发挥作用的亚群体。但我们对如何这些神经群在行为动物中协同工作，但仍然高度破碎。在提出的工作中，我们开发了一种计算方法来整合来自多个基因靶向神经群体并构建社会行为的回路模型控制。研究社会行为的一个重大挑战是其高度的可变性和复杂性，这些特征违背了传统的基于试验平均的神经活动分析。为了解决这个问题挑战，我们将利用我们最近开发的自动跟踪系统来构建成对自由的社会行为和感觉处理的定量和详细模型互动的老鼠。该行为模型将为全面的统计分析提供基础四个皮层下核团内部和之间的神经动力学，这些核团一起跨越三个假定的处理层，第一个刚刚经过感觉外围，最后一个刚刚经过导水管周围灰质中运动前群体的上游。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）表征使用线性非线性模型调整每个核中单个神经元的行为，以及 3) 拟合一个网络模型，用于对来自多个核的数据进行成像，并测试由此预测的连接性具有新颖的光遗传学微扰系统的模型。最后，我们将在这些分析的基础上通过皮层下核的网络模型解决行为控制的灵活性。在K99中阶段，这个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循环神经网络：随着实验室数量的增加，技能将变得越来越重要开始研究复杂的行为和中观神经回路。计算能力强加州理工学院的环境，包括 Pietro Perona、Markus Meister 和 Doris Tsao 的实验室，使其成为我进行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完善模型。数据呈现、教学、资助写作和其他方面的额外培训学生指导将使我能够过渡到独立职位。在独立R00 阶段，我将利用这些技能和我剩余的目标来建立一个专注于研究中尺度神经回路的灵活性和行为适应性。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Self-Supervised Keypoint Discovery in Behavioral Videos.

行为视频中自我监督的关键点发现。

DOI：
发表时间：
2022-06
期刊：
影响因子：
0
作者：
Sun, Jennifer J;Ryou, Serim;Goldshmid, Roni H;Weissbourd, Brandon;Dabiri, John O;Anderson, David J;Kennedy, Ann;Yue, Yisong;Perona, Pietro
通讯作者：
Perona, Pietro

The what, how, and why of naturalistic behavior.

自然主义行为的内容、方式和原因。

DOI：
发表时间：
2022-06
期刊：
影响因子：
5.7
作者：
Kennedy; Ann
通讯作者：
Ann

Pretraining Representations for Data-Efficient Reinforcement Learning

数据高效强化学习的预训练表示

DOI：
10.48550/arxiv.2404.05468
发表时间：
2021-06-09
期刊：
ArXiv
影响因子：
0
作者：
Max Schwarzer;Nitarshan Rajkumar;Michael Noukhovitch;Ankesh An;Laurent Charlin;Devon Hjelm;Philip Bachman;Aaron C. Courville
通讯作者：
Aaron C. Courville