STATISTICAL APPROACHES TO UNDERSTANDING NEURAL FEATURE SELECTIVITY

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 该项目的目标是使用先进的统计方法来表征神经元使用刺激的反应，这些刺激非常接近自然感觉环境中通常发现的刺激。首先，我们分析了视觉和听觉神经元的滤波特性。为了表征神经反应，我们使用一个模型，其中神经反应受到一个或两个相关刺激维度的影响。寻找这些刺激维度是分析中计算量最大的部分。我们使用模拟anneling和梯度上升搜索的组合，找到最合适的选择，这样的相关尺寸为每个神经元。这些结果有助于我们进一步了解物体识别，无论是视觉还是听觉，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