Collaborative Research: Dendritic Processing of Topographic Information in a Collision Detecting Neuron

专门处理感觉信息的大脑区域以其二维地图状（地形）组织而闻名。最近的证据表明，这种特性可以保持到亚细胞水平，以拓扑结构组织的形式输入到单细胞（神经元）的扩展分支（树突）。这个项目的目标是确定在一个原型神经元的树突处理这种地形输入的关键原则。 该项目的重点是一个神经细胞，是最敏感的物体接近碰撞过程中与动物，这是牵连在产生碰撞避免行为。 研究人员使用开发的工具来研究偏微分方程，以确定细胞离子通道的详细树突状分布。 接下来，技术旨在降低数学模型的复杂性，以及同时尊重地形的输入，提取一个简化的神经元表示？的信号处理特性。 这使得研究人员能够描述神经元树突在产生神经元对碰撞过程中接近的物体的反应中所起的作用。 特别关注的是理解对从不同方向接近的物体的反应的不变性。包括人类在内的所有动物的生存都严重依赖于神经元对即将发生的危险的处理。这个项目揭示了大脑如何在一个非常适合这一目的的特定模型系统中完成这一壮举。 生成数学工具，并提供给其他研究人员，以分析在其他环境中具有类似特性的神经元。 此外，该项目有助于我们对大脑功能的基本理解，最终可能导致更好地治疗影响感官知觉的疾病。 最后，该项目的结果可能在自主机器人的设计中找到应用。

The brain regions devoted to the processing of sensory information are remarkable for their two-dimensional map-like (topographic) organization. Recent evidence suggests that this property can be maintained down to the subcellular level, in the form of topographically organized inputs onto the extended arborizations (dendrites) of single cells (neurons). The goal of this project is to identify the key principles underlying the processing of such topographic inputs in the dendrites of a prototypical neuron. The project focuses on a nerve cell that is most sensitive to objects approaching on a collision course with the animal and that is implicated in generating collision avoidance behaviors. The investigators use tools developed to study partial differential equations to determine the detailed dendritic distribution of the cell's ion channels. Next, techniques designed to reduce the complexity of mathematical models are used, as well as to simultaneously respect the topography of inputs, to extract a simplified representation of the neuron?s signal processing characteristics. This allows the investigators to characterize the role played by the neuron's dendrites in generating the responses of the neuron to objects approaching on a collision course. Particular focus is on understanding the invariance of responses to objects approaching from different directions.The survival of all animals, including humans, critically depends on the neuronal processing of impending dangers. This project sheds light on how the brain accomplishes this feat in a particular model system ideally suited for this purpose. Mathematical tools are generated and are made available to other researchers to analyze neurons with similar properties in other contexts. Additionally, the project contributes to our basic understanding of brain function that may eventually lead to better cures for diseases affecting sensory perception. Finally, the results of this project could potentially find applications in the design of autonomous robots.