NONLINEAR FEATURE DIMENSIONS IN AUDITORY CORTEX

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。 我们希望利用计算设施来分析中枢听觉系统中神经元的反应。具体地说，我们希望使用一种新的计算方法来计算听觉神经元的响应函数。在之前与前加州大学旧金山分校、现供职于索尔克研究所的塔季扬娜·夏皮的合作下，超级计算机设备被用于执行类似的分析，尽管现在我们希望继续这一方法，但使用的是来自皮质下和皮质站的一组不同的数据。我们将实施的计算方法以前已经发表过(Atencio等人，2008年)。我们将计算听觉神经元的感受野。感受野描述了刺激和神经元反应之间的关系。感受场可以近似为一组线性过滤器，每个过滤器可以通过最大化刺激和神经尖峰反应之间的信息来计算。因此，每个过滤器被称为最大信息量维度(MID)。简而言之，第一个中间点是刺激空间中的方向或维度，它解释了刺激和反应之间最相互的信息。我们通过迭代过程得到第一个MID，其中，通过计算单个脉冲的出现和刺激在V上的投影之间的互信息来量化任何候选维度V的相关性。我们在刺激空间中搜索不同的方向直到收敛。一旦找到第一个中间点，我们就估计第二个中间点。第二个中间点是刺激空间中的维度，与第一个中间点一起，进一步最大化了信息。刺激大约有15,000个不同的刺激谱图，每个都有500个像素。刺激空间中的一个方向是谱图，其中谱图中的像素可以采用任何值。计算算法搜索整个刺激光谱图，直到它会聚到一个方向或图像，这是中间。由于谱图中的每个像素可能具有多个值，因此在该数据集上搜索和收敛到适当的像素值是计算密集型的，因此非常适合在超级计算机设施上实现。从之前与Sharpee博士一起使用超级计算机设备的工作中，我们知道为一个神经元计算两个MID大约需要160小时。因此，给定我们的数据集大小，我们希望请求75,000个小时的设施服务单元，以及2 TB的磁盘空间。我们与Sharpee博士合作的工作已经发表在一本影响很大的期刊上，我们预计这些进一步的计算也将取得同样的成果。Atencio CA，Sharpee T，Schreiner CE(2008)听觉皮质神经元中的协同非线性。神经元58：956-966。

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. We wish to utilize the computing facilities to analyze the responses of neurons in the central auditory system. Specifically, we wish to compute the response functions of auditory neurons using a novel computational methodology. Under a previous collaboration with Tatyana Sharpee, formerly of UCSF and now at the Salk Institute, the supercomputer facilities were utilized to perform a similar analysis, though now we would like to continue the methodology but on a different set of data from subcortical and cortical stations. The computational methodology we will implement has been previously published (Atencio et al., 2008). We will compute the receptive fields of auditory neurons. The receptive field describes the relationship between the stimulus and the response of a neuron. The receptive field can be approximated as a set of linear filters, each of which may be calculated by maximizing the information between the stimulus and the neural spiking response. Thus, each filter is termed a maximally informative dimension (MID). Briefly, the first MID is the direction, or dimension, in stimulus space that accounts for the most mutual information between the stimulus and the response. We obtain the first MID through an iterative procedure, where the relevance of any "candidate" dimension V is quantified by computing the mutual information between the occurrence of single spikes and projections of the stimulus onto V. We search through different directions in the stimulus space until convergence. Upon finding the first MID, we then estimate a second MID. The second MID is the dimension in the stimulus space that, together with the first MID, further maximizes the information. The stimulus is approximately 15,000 different stimulus spectrograms, each having 500 pixels. A direction in stimulus space is a spectrogram, where the pixels in the spectrogram may take on any value. The computational algorithm searches through the stimulus spectrograms till it converges to a single direction, or image, which is the MID. Since each pixel in a spectrogram may take on multiple values, searching and converging to the appropriate pixel values over this data set is computationally intensive, and thus ideally suited for implementation on the supercomputer facilities. From previous work with Dr. Sharpee, where she used the supercomputer facilities, we know that to calculate two MIDs for one neuron takes approximately 160 hours. Thus, given our data set size, we wish to request 75,000 hours of facility service units, as well as 2 terabyte of disk space. The work from our collaboration with Dr. Sharpee has already led to publication in an high impact journal, and we anticipate that these further computations will be similarly fruitful. Atencio CA, Sharpee T, Schreiner CE (2008) Cooperative nonlinearities in auditory cortical neurons. Neuron 58:956-966.