Evidence Optimization Techniques for Estimating Stimulus-Response Functions
Evidence Optimization Techniques for Estimating Stimulus-Response Functions
复制标题
估计刺激响应函数的证据优化技术
DOI：
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发表时间：
2002
期刊：
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通讯作者：
J. Linden
中科院分区：
文献类型：
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作者：
M. Sahani;J. Linden
理解感觉神经系统功能的一个重要步骤是尽可能准确地描述传递和处理感觉信息的神经元的刺激反应功能(SRF)。一种越来越常见的实验方法是向动物呈现快速变化的复杂刺激，同时记录一个或多个神经元的反应，然后直接估计输入的功能转换，这是神经元放电的原因。通常使用的估计技术，如Wiener滤波或其他基于Wiener或Volterra核的相关性估计，等同于高斯输出噪声回归模型中的最大似然估计。我们探索使用贝叶斯证据优化技术来满足这些估计的条件。我们表明，通过学习控制传递函数的光滑性和稀疏性的超参数，可以显著提高SRF估计的质量，这可以通过它们对新输入的响应的预测来衡量。
An essential step in understanding the function of sensory nervous systems is to characterize as accurately as possible the stimulus-response function (SRF) of the neurons that relay and process sensory information. One increasingly common experimental approach is to present a rapidly varying complex stimulus to the animal while recording the responses of one or more neurons, and then to directly estimate a functional transformation of the input that accounts for the neuronal firing. The estimation techniques usually employed, such as Wiener filtering or other correlation-based estimation of the Wiener or Volterra kernels, are equivalent to maximum likelihood estimation in a Gaussian-output-noise regression model. We explore the use of Bayesian evidence-optimization techniques to condition these estimates. We show that by learning hyper-parameters that control the smoothness and sparsity of the transfer function it is possible to improve dramatically the quality of SRF estimates, as measured by their success in predicting responses to novel input.