We present a general approach to the reconstruction of sensory stimuli encoded with leaky integrate-and-fire neurons with random thresholds. The stimuli are modeled as elements of a Reproducing Kernel Hilbert Space. The reconstruction is based on finding a stimulus that minimizes a regularized quadratic optimality criterion. We discuss in detail the reconstruction of sensory stimuli modeled as absolutely continuous functions as well as stimuli with absolutely continuous first-order derivatives. Reconstruction results are presented for stimuli encoded with single as well as a population of neurons. Examples are given that demonstrate the performance of the reconstruction algorithms as a function of threshold variability.

中文翻译：

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重建带有随机阈值的集成和发射神经元编码的感觉刺激。

我们提出了一种重建用具有随机阈值的泄漏整合和发射神经元编码的感觉刺激的一般方法。刺激被建模为再生核希尔伯特空间的元素。重建基于找到最小化正则化二次最优标准的刺激。我们详细讨论了建模为绝对连续函数以及具有绝对连续一阶导数的刺激的感官刺激的重建。为用单个和一组神经元编码的刺激呈现重建结果。给出的例子证明了作为阈值可变性函数的重建算法的性能。