We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the nonlinear input model, and the widely used spike-triggered covariance algorithm.

中文翻译：

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从峰值时间和视觉刺激的解码的复杂细胞的稀疏功能识别。

我们调查复杂细胞的稀疏功能识别和复杂细胞的合编编码的时空视觉刺激的解码。重构算法被公式化为秩最小化问题，该问题极大地减少了解码所需的采样测量（峰值）的数量。我们还建立了稀疏解码和功能识别之间的对偶，并提供了用于识别低秩树突刺激处理器的算法。对偶性使我们能够通过在输入空间中重构新的刺激来有效地评估我们的功能识别算法。最后，我们证明了我们的识别算法明显优于广义二次模型，非线性输入模型和广泛使用的尖峰触发协方差算法。