Quantitative tools for investigating sensory systems

描述(申请人提供)：了解感觉系统依赖于 表征神经元在每一阶段的刺激-反应特性 正在处理。这种特征可以用来研究神经。 感觉映射的潜在机制，并推导出 感官处理。这些知识将使我们能够更好地理解 通常由人脑执行的计算，特别是将 提供关于如何开发更好的感觉假体的见解。我们建议 开发一套新的定量方法来客观地表征 感觉神经元的非线性反应。我们方法的一个独特之处是 它们可以与复杂的自然主义刺激一起使用，也可以与 感觉神经生理学中常用的常规简单刺激。这个 非线性分析和潜在的复杂刺激的强大组合 使我们能够表征感觉神经元，即使在相对较高的水平 感官处理。我们的大部分建议都集中在开发 我们的分析所需的定量计算工具。首先，我们建议 开发用于估计非线性感受野轮廓的算法 感觉神经元对任意刺激的反应。第二，我们建议 开发工具来评估所产生的接受场的质量 模特们。第三，我们将开发分析工具，以便 对衍生模型的生物学解释。最后，我们建议 开发一个全面的软件包，它将包括线性和 非线性估计技术以及评估和分析工具。 该包将由一个独立的文档函数库组成，用于 最有经验的用户，具有集成图形用户的交钥匙包 面向一般用户的界面和丰富的在线文档。工具和 分析将使用计算模型和在 我们的实验室和其他同意帮助我们的实验室 软件测试和评估。

DESCRIPTION (provided by applicant): Understanding sensory systems relies on characterizing the stimulus-response properties of neurons at each stage of processing. This characterization can then be used to investigate the neural mechanisms underlying sensory maps and to derive computational models of sensory processing. Such knowledge will enable us to better understand the computations performed by the human brain in general and in particular will provide insights on how to develop better sensory prosthetics. We propose to develop a novel suite of quantitative methods for objectively characterizing the nonlinear responses of sensory neurons. A unique feature of our methods is that they can be used with complex, naturalistic stimuli as well as with conventional simple stimuli commonly used in sensory neurophysiology. The powerful combination of nonlinear analysis and complex stimuli potentially enables us to characterize sensory neurons even at relatively high levels of sensory processing. Much of our proposal focuses on developing the quantitative computational tools necessary for our analyses. First, we propose to develop algorithms for estimating nonlinear receptive field profiles of sensory neurons from responses to arbitrary stimuli. Second, we propose to develop tools for evaluating the quality of the resulting receptive field models. Third, we will develop analysis tools that will facilitate the biological interpretation of the derived models. Finally, we propose to develop a comprehensive software package that will include both linear and nonlinear estimation techniques as well as the evaluation and analysis tools. The package will consist of a stand alone documented function library for the most experienced users, a turn-key package with an integrated graphical user interface for general users and extensive online documentation. The tools and analyses will be validated using computational models and data collected in our laboratories and in other laboratories that have agreed to aid us in software testing and evaluation.