Maximum likelihood estimation of cascade point-process neural encoding models
Maximum likelihood estimation of cascade point-process neural encoding models
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DOI：
10.1088/0954-898x/15/4/002
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发表时间：
2004-11-01
影响因子：
7.8
通讯作者：
Paninski, L
中科院分区：
文献类型：
--
作者：
Paninski, L
最近的工作研究了刺激驱动的神经活动模型的估计，其中一些线性滤波过程之后是一个非线性的，概率尖峰阶段。我们分析了一个这样的模型，这个非线性步骤是由一个已知的参数函数实现的估计，这个函数是已知的速度估计过程相当的假设。我们调查这种类型的模型的似然函数的形状，给出一个简单的条件，确保没有非全球性的局部最大值存在的可能性，从而导致有效的算法计算的最大似然估计，并讨论了所允许的非线性形式的影响。最后，我们注意到一些有趣的连接之间的似然为基础的估计和经典的尖峰触发的平均估计，讨论了一些有用的扩展的基本模型结构，并提供了两个新的应用程序的生理数据。
Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a known parametric function; the assumption that this function is known speeds the estimation process considerably. We investigate the shape of the likelihood function for this type of model, give a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood-leading, in turn, to efficient algorithms for the computation of the maximum likelihood estimator-and discuss the implications for the form of the allowed nonlinearities. Finally, we note some interesting connections between the likelihood-based estimators and the classical spike-triggered average estimator, discuss some useful extensions of the basic model structure, and provide two novel applications to physiological data.