论文标题
尖峰触发的下降
Spike-Triggered Descent
论文作者
论文摘要
神经对感觉刺激的反应的表征是神经科学中的核心问题。尖峰触发的平均水平（STA）是一种影响力的技术，已用于在各种动物受试者中提取最佳的线性内核。但是，当未达到模型假设时，它可能会导致误导和不精确的结果。我们引入了一种称为Spike触发下降（STD）的技术，该技术可单独或与STA结合使用，以在STA失败的情况下提高精度并产生成功。 STD通过模拟模型神经元的模型来工作，该神经元学会重现观察到的尖峰火车。通过参数优化实现了学习，该参数优化依赖于建模为新型内部产品空间的尖峰列车空间中引起的度量。该技术可以精确地使用有限的数据学习高阶内核。从刺激性迁移神经数据集中提取的内核证明了这种方法的强度。
The characterization of neural responses to sensory stimuli is a central problem in neuroscience. Spike-triggered average (STA), an influential technique, has been used to extract optimal linear kernels in a variety of animal subjects. However, when the model assumptions are not met, it can lead to misleading and imprecise results. We introduce a technique, called spike-triggered descent (STD), which can be used alone or in conjunction with STA to increase precision and yield success in scenarios where STA fails. STD works by simulating a model neuron that learns to reproduce the observed spike train. Learning is achieved via parameter optimization that relies on a metric induced on the space of spike trains modeled as a novel inner product space. This technique can precisely learn higher order kernels using limited data. Kernels extracted from a Locusta migratoria tympanal nerve dataset demonstrate the strength of this approach.
