Probing neural feature selectivity with natural stimuli

用自然刺激探测神经特征选择性

• 批准号: 6774651
• 负责人: Tatyana O. SHARPEE
• 金额: $ 11.02万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-07 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6774651
• 关键词: biophysics; cats; information theory; mathematics; method development; neural information processing; neural transmission; neurophysiology; statistics /biometry; stimulus /response; visual cortex; visual stimulus

DESCRIPTION (provided by applicant): The candidate seeks to become independent as a neuroscientist and to work at the interface between neuroscience and physics. The Sloan-Swartz Center at UCSF provides a unique opportunity to become immersed in neuroscience at its cutting edge. While the majority of her time will be spent in research, she will attend a range of systems neuroscience and computer vision courses. The immediate goals of her research are to develop an information-theoretic method that allows for a rigorous statistical analysis of neural responses to natural stimuli, which are non- Gaussian and have strong spatiotemporal correlations; and to use this method to analyze the responses of visual cortical neurons to natural time-varying images. There are numerous indications that the responses of neurons to natural stimuli cannot be completely predicted from their responses to stimuli with simple statistical properties, such as white noise ensembles. However, existing methods for analyzing single neuron responses may be rigorously applied only to Gaussian ensembles. The information-theoretic method involves finding the stimulus dimensions that carry the most information about the neuron's response. The stimulus ensemble is not assumed to be Gaussian. The only assumption made is that the neuron is selective for a small number of stimulus dimensions out of the high-dimensional stimulus space: responses with respect to the relevant dimensions might be arbitrarily nonlinear. After testing the method on model neurons and verifying that it gives reasonable results for cortical neurons probed by natural scenes, the method will be used to test the categorical distinction between simple and complex cells in the primary visual cortex. The method will then be modified so that a large set of directions related to each other via certain symmetry operations, such as translation or scaling, can be simultaneously found, with the goal of systematically probing neurons in extrastriate areas with natural scenes.

描述（由申请人提供）：候选人寻求成为一名独立的神经科学家，并在神经科学和物理学之间的交叉领域工作。加州大学旧金山分校的斯隆-斯沃茨中心提供了一个独特的机会，让您沉浸在神经科学的前沿。虽然她的大部分时间将花在研究上，但她将参加一系列系统神经科学和计算机视觉课程。她研究的直接目标是开发一种信息论方法，可以对自然刺激的神经反应进行严格的统计分析，这些反应是非高斯的，并且具有很强的时空相关性；并利用该方法分析视觉皮层神经元对自然时变图像的反应。有许多迹象表明，神经元对自然刺激的反应无法通过简单的统计特性（例如白噪声系综）对刺激的反应来完全预测。然而，用于分析单个神经元响应的现有方法可能仅严格应用于高斯系综。信息论方法涉及找到携带有关神经元反应的最多信息的刺激维度。刺激系综不被假定为高斯分布。唯一的假设是神经元对高维刺激空间中的少量刺激维度具有选择性：相对于相关维度的响应可能是任意非线性的。在模型神经元上测试该方法并验证其为自然场景探测的皮层神经元提供合理的结果后，该方法将用于测试初级视觉皮层中简单细胞和复杂细胞之间的分类区别。然后对该方法进行修改，以便可以同时找到通过某些对称操作（例如平移或缩放）彼此相关的大量方向，其目标是系统地探测具有自然场景的纹外区域中的神经元。