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: 10058915
• 负责人: John H Byrne
• 金额: $ 302.21万
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058915
• 关键词: Adaptive Behaviors; Address; Affect; Anatomy; Animals; Aplysia; Arousal; Behavior; Behavior Control; Behavioral; Biological Models; Brain; Cerebrum; Choices and Control; Code; Combinatorics; Complex; Computer Models; Data; Deglutition; Electrodes; Elements; Environment; Failure; Feedback; Feeding behaviors; Food; Future; Ganglia; Hodgkin-Huxley model; Human; Implant; Interneurons; Investigation; Learning; Mechanics; Mediating; Memory; Michigan; Modeling; Monitor; Motivation; Motor; Motor Neurons; Movement; Nervous system structure; Neural Network Simulation; Neuromechanics; Neurons; Pattern; Preparation; Process; Regulation; Research; Role; Satiation; Specific qualifier value; Spinal Cord; Stimulus; Synapses; System; Techniques; Testing; Texas; Vertebrates; Work; base; behavior prediction; biomechanical model; carbon fiber; combinatorial; connectome; feeding; food surveillance; improved; insight; mathematical analysis; mechanical load; motor behavior; multidisciplinary; neural circuit; neural model; neuronal patterning; new technology; novel; novel strategies; predictive modeling; relating to nervous system; response; sensory input; sensory stimulus; success; voltage sensitive dye

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.