A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop system

一种在易处理的小脑闭环系统中分析功能连接组学和组合控制的新方法

• 批准号: 10700737
• 负责人: John H Byrne
• 金额: $ 89.26万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700737
• 关键词: novel; approach; analyzing; functional; connectomics

SUMMARY Adaptive behaviors emerge from neuronal networks by dynamically regulating functional connectomes. Based on an underlying anatomical connectome, a functional connectome is the configuration of effective synaptic connections that underlies a pattern of neuronal activity during a specific behavior. Unique combinations of neurons activate specific functional connectomes, thereby generating a behavior (a combinatoric code). By combining neural network and biomechanical modeling, intracellular recording, and newly developed large-scale recording techniques, we will analyze functional connectomes and their combinatoric control of behavior, and how local plasticity and global dynamics mediate feeding behavior, which is controlled by a small brain system. The research will be performed by a multidisciplinary team consisting of Drs. J. Byrne (U. Texas, Houston), C. Chestek (U. Michigan, Ann Arbor), H. Chiel (CWRU), E. Cropper (Mt. Sinai), A. Susswein (Bar Ilan U.), P. Thomas (CWRU) and K. Weiss (Mt. Sinai). The project will: 1) develop a predictive neuromechanical model that incorporates a biomechanical model of the feeding musculature with a computational model of the feeding neural circuitry; 2) use large-scale and intracellular recording techniques to analyze the functional connectome and combinatoric control for choices among different feeding behaviors in response to sensory stimuli; and 3) use these recording techniques to analyze the ways in which the functional connectome and its combinatoric control are reconfigured by modulatory factors, motivation, and learning. We also will examine the ways in which arousal and satiation change the bias of the functional connectome and thus alter behavior, and the ways in which learning may add or remove elements of the functional connectome as an animal modifies behavior to respond to changes in the environment. The results will provide insights into how processes at multiple levels of neural organization contribute to regulation of behavior. Such studies in a small brain model system will provide insights that will help guide future investigations in more complex systems, such as vertebrates and humans.

概括 通过动态调节功能连接组，从神经元网络中出现自适应行为。基于 在基本的解剖连接组上，功能连接组是有效突触的配置 在特定行为期间神经元活动模式的基础的连接。独特的组合 神经元激活特定的功能连接，从而产生行为（组合代码）。经过 结合神经网络和生物力学建模，细胞内记录和新开发的大规模 记录技术，我们将分析功能连接及其对行为的组合控制，以及 局部塑性和全球动力学如何介导喂养行为，这是由小型大脑系统控制的。 该研究将由由DRS组成的多学科团队进行。 J.伯恩（美国德克萨斯州，休斯顿），C。 Chestek（U. Michigan，Ann Arbor），H。Chiel（CWRU），E。Cropper（西奈山），A。Susswein（Bar Ilan U.），P。 托马斯（CWRU）和K. Weiss（西奈山）。该项目将：1）开发一个预测性神经力学模型 将进食肌肉组织的生物力学模型与进食神经的计算模型结合在一起 电路； 2）使用大规模和细胞内记录技术分析功能连接组和 响应感觉刺激的不同喂养行为之间选择的组合控制； 3）使用 这些记录技术以分析功能连接组及其组合控制的方式 通过调节因素，动机和学习来重新配置。我们还将研究唤醒的方式 饱满改变了功能连接的偏见，从而改变了行为，以及学习的方式 可能会添加或删除功能连接组的元素，因为动物会改变行为以响应变化 在环境中。结果将提供有关神经组织多个层次的过程的见解 有助于行为调节。小脑模型系统中的此类研究将提供见解，以帮助 指导在更复杂的系统（例如脊椎动物和人类）中进行未来的研究。