Analyzing multicomponent receptive fields from neural responses to natural stimuli.
Analyzing multicomponent receptive fields from neural responses to natural stimuli.
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分析从神经反应到自然刺激的多组分接受场。
DOI：
10.3109/0954898x.2011.566303
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Sharpee TO
中科院分区：
文献类型：
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作者：
Rowekamp RJ;Sharpee TO
建立越来越好的自然刺激神经反应模型的挑战是准确地估计可能共同影响神经尖峰概率的多个刺激特征。特征组合的选择性被认为是实现神经响应的经典特性（例如对比度不变性）的关键。这些多个刺激特征的联合搜索是困难的，因为估计尖峰概率作为候选相关维度上的刺激投影的多维函数受到维度灾难的影响。一个有吸引力的替代方法是顺序搜索相关维度，如投影寻踪回归。在这里，我们证明了使用分析参数和模型细胞的模拟，不同类型的顺序搜索策略表现出系统的偏见时，与自然刺激。仿真结果表明，联合优化是可行的，最多三维与现有的算法。当应用于V1神经元对自然场景的响应时，与顺序优化的维度相比，基于三个联合优化维度的模型在大多数情况下具有更好的预测能力，不同的顺序方法产生了可比的结果。因此，尽管维数灾难仍然存在，但至少可以通过联合信息最大化来估计几个相关维度。
The challenge of building increasingly better models of neural responses to natural stimuli is to accurately estimate the multiple stimulus features that may jointly affect the neural spike probability. The selectivity for combinations of features is thought to be crucial for achieving classical properties of neural responses such as contrast invariance. The joint search for these multiple stimulus features is difficult because estimating spike probability as a multidimensional function of stimulus projections onto candidate relevant dimensions is subject to the curse of dimensionality. An attractive alternative is to search for relevant dimensions sequentially, as in projection pursuit regression. Here we demonstrate using analytic arguments and simulations of model cells that different types of sequential search strategies exhibit systematic biases when used with natural stimuli. Simulations show that joint optimization is feasible for up to three dimensions with current algorithms. When applied to the responses of V1 neurons to natural scenes, models based on three jointly optimized dimensions had better predictive power in a majority of cases compared to dimensions optimized sequentially, with different sequential methods yielding comparable results. Thus, although the curse of dimensionality remains, at least several relevant dimensions can be estimated by joint information maximization.