NONLINEAR FEATURE DIMENSIONS IN AUDITORY CORTEX

该子项目是利用 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来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 我们希望利用计算机设备来分析中枢听觉系统神经元的反应。具体来说，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Tatyana Sharpee的合作下，以前在加州大学旧金山分校，现在在索尔克研究所，超级计算机设备被用来执行类似的分析，虽然现在我们想继续这种方法，但来自皮层下和皮层站的不同数据集。我们将实施的计算方法先前已经发表（Atencio等人，2008年）。我们将计算听觉神经元的感受野。感受野描述了神经元的刺激和反应之间的关系。感受野可以近似为一组线性滤波器，每个线性滤波器可以通过最大化刺激和神经尖峰响应之间的信息来计算。因此，每个滤波器被称为最大信息维度（MID）。简单地说，第一个MID是刺激空间中的方向或维度，其解释了刺激和响应之间的最多互信息。我们通过迭代过程获得第一个MID，其中任何“候选”维度V的相关性通过计算单个尖峰的发生和刺激到V上的投影之间的互信息来量化。我们在刺激空间中搜索不同的方向，直到收敛。在找到第一个MID后，我们估计第二个MID。第二MID是刺激空间中的维度，其与第一MID一起进一步最大化信息。刺激是大约15，000个不同的刺激频谱图，每个具有500个像素。刺激空间中的方向是谱图，其中谱图中的像素可以取任何值。计算算法搜索刺激频谱图，直到它收敛到单个方向或图像，即MID。由于频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Sharpee博士之前使用超级计算机设备的工作中，我们知道计算一个神经元的两个MID大约需要160个小时。因此，考虑到我们的数据集大小，我们希望请求75 000小时的设施服务单位，以及2 TB的磁盘空间。我们与Sharpee博士合作的工作已经在一份高影响力的期刊上发表，我们预计这些进一步的计算将同样富有成效。Atencio CA，Sharpee T，Schreiner CE（2008）听觉皮层神经元的合作非线性。Neuron 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.