Analyzing neural responses to natural signals: Maximally informative dimensions
Analyzing neural responses to natural signals: Maximally informative dimensions
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DOI：
10.1162/089976604322742010
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发表时间：
2004-02-01
影响因子：
2.9
通讯作者：
Bialek, W
中科院分区：
文献类型：
--
作者：
Sharpee, T;Rust, NC;Bialek, W
我们提出了一种方法，允许对非高斯和表现出强相关性的自然刺激的神经反应进行严格的统计分析。我们有一个模型，在这个模型中，神经元从高维刺激空间中选择少量的刺激维度，但在这个子空间中，响应可以是任意非线性的。现有的分析方法是基于刺激和反应之间的相关函数，但这些方法只能保证在高斯刺激集合的情况下工作。作为相关函数的替代方案，我们最大化了神经反应和刺激在低维子空间上的投影之间的相互信息。这个过程可以通过增加这个子空间的维数来迭代地完成。那些允许恢复峰值和完整未投射刺激之间的所有信息的维度描述了相关的子空间。如果相关子空间的维数确实很小，则即使在完全自然刺激条件下，也可以映射神经元的输入-输出函数。这些思想分别在模型视觉和听觉神经元对自然场景和声音的响应的仿真中得到说明。
We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli that are nongaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high-dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low-dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual and auditory neurons responding to natural scenes and sounds, respectively.