CRCNS: Dynamics and Plasticity of a Neuromechanical System

一些小的神经元网络因其执行多种功能的能力而引人注目。理解将反馈整合到特征中的规则是一个挑战，比如活跃神经元的放电模式之间的相位关系，以及一个小网络如何从一个特征行为“切换”到另一个特征行为。尽管研究正在阐明学习的细胞和分子机制，但要理解单个神经元特性的变化如何改变整个神经回路的活动，进而改变动物的整体行为，却更加困难。这个项目是一个合作使用计算，理论和实验的方法来分析海洋软体动物，海兔的摄食行为。这种动物吞下食物时，用无颚的颚块有节奏地发出嘶嘶声和吮吸声，由大约130个运动神经元和中间神经元组成的网络控制；如果潜在的食物通过它的物理特性被感觉为不可食用，肌肉活动的模式就会从摄入到排斥食物发生变化。总体目标是确定单个神经细胞特性的微小变化是如何在学习后观察到的摄食行为的巨大变化。具体目标1是构建口腔质量（有限元法）、其神经控制（连续时间递归神经网络（CTRNN），运动和感觉神经元采用霍奇金-赫胥黎模型）和不可食用食物的动力学数学模型，并进行实验研究，以提高对模型中每个组成部分的理解。该模型的重点是再现在反复接触不可食用食物时观察到的运动模式的变化。具体目标2是为新的动力学模型开发一个数值最优控制器，并使用它来预测时间、相位和神经输入强度的微小变化对口腔肿块产生的行为的影响。这里的重点是神经末梢的生物力学如何影响神经控制器的设计特性。具体目标1和2中开发的模型将用于分析单个神经元对稳定节奏行为的移位联盟的贡献，并预测突触强度或内在属性的局部变化对神经回路整体动态的重要性，无论是在孤立的情况下还是在与生物力学模型连接时。在具体目标3下，将设计实验研究来测试模拟研究的这些预测。这些实验研究将记录完好无损的动物在得知食物不可食用时的神经活动，以及在特定神经细胞的活动受到干扰后减少的准备（表现出进食样的运动）。这项工作的影响将超越计算神经科学和行为神经科学，到无脊椎动物生理学、工程学、机器人和控制系统。首先，它很可能为理解局部神经活动变化对动物整体行为的影响提供原理。其次，它可以为设备的设计原则提供建议，这些设备可以在分散注意力的输入下持续追求特定目标，同时，如果在正确的环境中出现适当的刺激，则可以非常灵活地改变行为。第三，这些原理可能会成为新型仿生机器人和控制装置的基础。此外，学生和合作者将共同参与跨学科的方法和技术，这将加强对下一代科学家的培训。

Some small networks of neurons are remarkable for their ability to execute multiple functions. It has been a challenge to understand what the rules are for integrating feedback into features such as phase relationships among the firing patterns of active neurons, and how a small network can 'switch' from one characteristic behavior to another. Although research is clarifying the cellular and molecular mechanisms of learning, it has been more difficult to understand how changes in the properties of individual neurons change the activity of a whole neural circuit, and in turn alter an animal's overall behavior. This project is a collaboration using computational, theoretical and experimental approaches to analyze the feeding behavior of a marine mollusk, the sea hare Aplysia. This animal ingests food with rhythmic rasping and sucking motions of a jawless buccal mass, run by a network of about 130 motor neurons and interneurons; if potential food is sensed by its physical properties as inedible, the pattern of muscle activity changes from ingestion to food rejection. The overall goal is to determine how small changes in the properties of individual nerve cells create the large changes in feeding behavior that are observed after learning. Specific Aim 1 is to construct a kinetic mathematical model of the buccal mass (finite-element method), its neural control (continuous-time recurrent neural network (CTRNN), with Hodgkin-Huxley models for motor and sensory neurons), and inedible food, and to conduct experimental studies to improve the understanding of each of these components of the model. The focus of this modeling is to reproduce the changes in motor pattern observed during repeated encounters with inedible food. Specific Aim 2 is to develop a numerically optimal controller for the new kinetic model, and use it to predict the effects of small changes in timing, phasing and intensity of neural input on the behavior generated by the buccal mass. The focus here is on how the biomechanics of the periphery influences the design properties of the neural controller. The models developed in Specific Aims 1 and 2 will be used to analyze the contributions of individual neurons to the shifting coalitions that stabilize the rhythmic behavior, and to predict the importance of local changes in synaptic strengths or intrinsic properties to the overall dynamics of the neural circuit both in isolation and when it is connected to the biomechanical model. Under Specific Aim 3, experimental studies will be designed to test these predictions from simulation studies. These experimental studies will record neural activity in intact animals as they learn that food is inedible, and in reduced preparations (that show feeding-like movements) after perturbations of the activity of specific nerve cells. This work will have an impact beyond computational neuroscience and behavioral neuroscience, to invertebrate physiology, engineering, robotics and control systems. First, it is likely to generate principles for understanding the effects of localized changes in neural activity on an animal's overall behavior. Second, it may suggest design principles for devices that can persistently pursue a specific goal despite distracting inputs, and at the same time be remarkably flexible and change behavior if an appropriate stimulus occurs in the correct context. . Third, these principles are likely to serve as the basis for novel biologically-inspired robotic and control devices. In addition, students and collaborators will be involved together in cross-disciplinary approaches and techniques that will enhance training for the next generation of scientists.