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。 理解将反馈整合到诸如活跃神经元的放电模式之间的相位关系等特征中的规则，以及小型网络如何从一种特征行为“切换”到另一种特征行为，一直是一个挑战。虽然研究正在澄清学习的细胞和分子机制，但更难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。该项目是一个合作项目，使用计算，理论和实验方法来分析海洋软体动物，海兔的摄食行为。 这种动物摄取食物时，会有节奏地用无颚的颊部肿块发出粗声和吮吸的动作，这些动作由大约130个运动神经元和中间神经元组成的网络控制;如果潜在的食物被其物理特性感知为不可食用，肌肉活动的模式就会从摄取转变为拒绝食物。 总体目标是确定单个神经细胞特性的微小变化如何在学习后观察到的进食行为中产生巨大变化。 具体目标1是构建一个动力学的数学模型的颊质量（有限元法），其神经控制（连续时间递归神经网络（CTRNN），与运动和感觉神经元的Hodgkin-Huxley模型），和不可食用的食物，并进行实验研究，以提高这些组件的模型的理解。 这种建模的重点是再现在反复遇到不可食用的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，并使用它来预测颊部肿块所产生的行为上的神经输入的定时、定相和强度的小变化的影响。 这里的重点是如何外围的生物力学影响的神经控制器的设计性能。 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律行为的移位联盟的贡献，并预测突触强度或内在特性的局部变化对神经回路整体动力学的重要性，无论是孤立的还是连接到生物力学模型时。 在具体目标3下，将设计实验研究，以检验模拟研究的这些预测。这些实验研究将记录完整动物的神经活动，因为它们知道食物是不可食用的，以及在特定神经细胞的活动受到干扰后减少的准备工作（显示喂食样运动）。这项工作将产生超越计算神经科学和行为神经科学的影响，对无脊椎动物生理学，工程学，机器人和控制系统。首先，它可能会产生一些原则，用于理解神经活动中局部变化对动物整体行为的影响。第二，它可能建议设备的设计原则，可以持续追求一个特定的目标，尽管分散注意力的输入，并在同一时间是非常灵活的，并改变行为，如果一个适当的刺激发生在正确的上下文中。.第三，这些原理很可能成为新型生物启发机器人和控制设备的基础。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.