Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model
Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model
复制标题
DOI：
10.1162/0899766042321797
复制
发表时间：
2004-12-01
影响因子：
2.9
通讯作者：
Simoncelli, EP
中科院分区：
文献类型：
--
作者：
Paninski, L;Pillow, JW;Simoncelli, EP
我们研究了神经响应的级联编码模型，其中线性过滤阶段之后是噪声、泄漏、积分和激发尖峰生成机制。该模型为基于泊松（无记忆）尖峰生成的模型提供了一种在生物物理上更现实的替代方案，并且可以有效地重现体内观察到的各种尖峰行为。我们描述了模型参数的最大似然估计，仅给出细胞外尖峰序列响应（而不是细胞内电压数据）。具体来说，我们证明对数似然函数是凹的，因此具有本质上唯一的全局最大值，可以使用梯度上升技术找到该最大值。我们开发了一种用于计算最大似然解的有效算法，通过数值模拟证明了所得估计器的有效性，并讨论了使用时间重新缩放和密度演化技术来测试模型有效性的方法。
We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrate-and-fire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors seen in vivo. We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). Specifically, we prove that the log-likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques. We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model's validity using time-rescaling and density evolution techniques.