Quantitative tools for investigating sensory systems

描述（由申请人提供）：理解感觉系统依赖于 表征神经元在每个阶段的刺激反应特性， 处理.然后，这种表征可以用于研究神经 机制的感觉地图，并得出计算模型， 感觉处理这些知识将使我们能够更好地了解 一般来说，人类大脑执行的计算， 为如何开发更好的感官修复术提供见解。我们建议 开发一套新的定量方法， 感觉神经元的非线性反应。我们的方法的一个独特之处是 它们可以与复杂的、自然主义的刺激一起使用，也可以与 通常用于感觉神经生理学的常规简单刺激。的 非线性分析和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， 感觉处理我们的大部分建议侧重于发展 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，用于评估由此产生的感受野的质量 模型第三，我们将开发分析工具， 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，将包括线性和 非线性估计技术以及评估和分析工具。 该软件包将包含一个独立的文档化函数库， 最有经验的用户，一个交钥匙包与集成的图形用户 一般用户的界面和广泛的在线文档。的工具和 分析将使用计算模型和收集的数据进行验证， 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.