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

负责处理感觉信息的大脑区域因其二维地图状(地形图)组织而引人注目。最近的证据表明，这一特性可以在亚细胞水平上保持下去，形式是在单个细胞(神经元)的扩展树枝(树突)上进行地形组织的输入。该项目的目标是确定在典型神经元的树突中处理这种地形输入的关键原理。该项目的重点是一个神经细胞，它对与动物相撞的物体最敏感，并与产生碰撞避免行为有关。研究人员使用研究偏微分方程式的工具来确定细胞离子通道的详细树枝状分布。其次，采用旨在降低数学模型复杂性的技术，以及同时尊重输入的拓扑学，提取神经元的简化表示？S信号处理特性。这使得研究人员能够表征神经元的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来自不同方向的物体的反应的不变性。包括人类在内的所有动物的生存关键取决于对即将到来的危险的神经元处理。这个项目揭示了大脑是如何在一个特别的模型系统中完成这一壮举的，该模型系统非常适合于这一目的。数学工具被生成，并被其他研究人员用来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了解，最终可能导致更好地治疗影响感官知觉的疾病。最后，该项目的结果可能会在自主机器人的设计中找到潜在的应用。

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.