STATISTICAL APPROACHES TO UNDERSTANDING NEURAL FEATURE SELECTIVITY

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 这个项目的目标是使用先进的统计方法来描述神经元的反应，使用与自然感觉环境中通常发现的刺激非常接近的刺激。首先，我们分析视觉和听觉神经元的过滤特性。为了表征神经反应，我们使用了一个模型，其中神经反应受到一个或两个相关刺激维度的影响。找到这些刺激维度是分析中计算最密集的部分。我们使用模拟更新法和梯度上升搜索法的组合来为每个神经元找到这种相关维度的最合适的选择。这些结果有助于我们进一步理解物体识别，无论是视觉的还是听觉的，是如何在神经系统中进行的

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The goal of this project is to use advanced statistical methods for characterizing responses of neurons using stimuli that closely approximate those typically found in the natural sensory environment. Primarily we analyze filtering properties of visual and auditory neurons. To characterize neural responses we use a model where neural responses are affected by one or two relevant stimulus dimensions. Finding these stimulus dimensions is the most computationally intensive part of the analysis. We use a combination of simulated anneling and gradient ascent search to find the most appropriate selection of such relevant dimensions for each neuron. These results help further our understanding of how object recognition, eithr visual or auditory, is carried out in the nervous system