BIOPHYSICAL CHANNEL MODELS OF ASSOCIATIVE LEARNING

这是一项科学家发展奖的申请。的目标是 拟议的研究将开始以定量的细节理解， 粗角隐翅虫的学习和记忆机制 无脊椎动物海螺，具有相同的行为特征和 学习和记忆的生物物理和生化机制 哺乳动物。赫米森达展示了联想学习对 光(条件刺激)和湍流的成对呈现 (无条件刺激)。这种记忆的获取和存储发生了 我是B内侧的 光感受器细胞，光诱发信号的会聚部位 以及由湍流引起的信号。尽管从长远来看， 表征了对应于存储器存储的B细胞膜 一些生物物理机制的基本细节 联想学习仍然未知，CS和UCS相互作用 导致联想学习的行为特征不是 完全理解。要实现对以下方面的深入了解 这些基本问题，研究计划描述了一个相互关联的 组织了一系列生物物理实验和数学建模 围绕以下三个问题：(1)光的什么性质 感应电流使刺激编码足够敏感，从而允许 在《赫米森达》中看到的刺激特异性程度？(2)有没有 突触前机制和突触后机制 学习诱导毛细胞刺激作用的改变 光感受器？(3)每个电流的功能作用是什么， 以及电流之间的相互作用相对于行为 经典条件反射的特点？生物物理实验 而解决这些问题所需的数学建模是必要的 和足够的培训使我能够成为一个独立的 并在以下领域做出重大贡献 计算神经科学。对生物物理学的定量认识 和生物化学相互作用在联想学习中的基础 由拟议的研究产生的赫米森达也将有助于 对哺乳动物联想学习的理解。简明的发展 对河道模型的数学描述将使 制定改进的学习规则、网络架构和 人工神经网络中的神经元元件。

This is a request for a Scientist Development Award. The goal of the proposed research is to begin to understand, in quantitative detail, mechanisms of learning and memory in Hermissenda crassicornis, an invertebrate marine snail that shares behavioral characteristics and biophysical and biochemical mechanisms of learning and memory with mammals. Hermissenda demonstrates associative learning in response to paired presentations of light (Conditioned Stimulus) and turbulence (Unconditioned Stimulus). Acquisition and storage of this memory occurs m the medial B photoreceptor cell, the site of convergence of the light-evoked signal and the turbulence-evoked signal. Although the long term changes in the B cell membrane corresponding to memory storage have been characterized essential details of some of the biophysical mechanisms underlying associative learning are still unknown, and the CS and UCS interactions leading to the behavioral characteristics of associative learning are not completely understood. To achieve an in-depth understanding of some of these essential issues, the research plan describes an inter-related series of biophysical experiments and mathematical modeling organized around the following three questions: (1) What properties of the light induced currents makes stimulus coding sufficiently sensitive to permit the degree of stimulus specificity seen in Hermissenda? (2) Are there pre-synaptic mechanisms, as well as post-synaptic mechanisms, underlying the learning induced change in the effect of hair cell stimulation on the B photoreceptor? (3) What is the functional role of each of the currents, and of interactions among currents with respect to behavioral characteristics of classical conditioning? The biophysical experiments and mathematical modeling required to address these issues are necessary and sufficient training to enable me to become an independent investigator and to make significant contributions in the field of computational neuroscience. Quantitative understanding of biophysical and biochemical interactions underlying association learning in Hermissenda produced by the proposed research also will contribute to understanding of associative learning in mammals. Development of concise mathematical descriptions of the channel models would make possible the formulation of improved learning rules, network architectures and neuronal elements in artificial neural networks.