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首页> 外文期刊>Journal of Computational Neuroscience >Correlation-distortion based identification of Linear-Nonlinear-Poisson models
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Correlation-distortion based identification of Linear-Nonlinear-Poisson models

机译：基于相关失真的线性-非线性-泊松模型辨识

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摘要

Linear-Nonlinear-Poisson (LNP) models are a popular and powerful tool for describing encoding (stimulus-response) transformations by single sensory as well as motor neurons. Recently, there has been rising interest in the second- and higher-order correlation structure of neural spike trains, and how it may be related to specific encoding relationships. The distortion of signal correlations as they are transformed through particular LNP models is predictable and in some cases analytically tractable and invertible. Here, we propose that LNP encoding models can potentially be identified strictly from the correlation transformations they induce, and develop a computational method for identifying minimum-phase single-neuron temporal kernels under white and colored random Gaussian excitation. Unlike reverse-correlation or maximum-likelihood, correlation-distortion based identification does not require the simultaneous observation of stimulus-response pairs-only their respective second order statistics. Although in principle filter kernels are not necessarily minimum-phase, and only their spectral amplitude can be uniquely determined from output correlations, we show that in practice this method provides excellent estimates of kernels from a range of parametric models of neural systems. We conclude by discussing how this approach could potentially enable neural models to be estimated from a much wider variety of experimental conditions and systems, and its limitations.
机译：线性-非线性-泊松（LNP）模型是一种流行且功能强大的工具，用于描述由单个感觉神经元和运动神经元进行的编码（刺激-响应）转换。近来，人们对神经尖峰序列的二阶和高阶相关结构及其与特定编码关系的关系越来越感兴趣。通过特定的LNP模型进行转换时，信号相关的失真是可以预测的，在某些情况下可以分析得出可逆的。在这里，我们建议可以从LNP编码模型引起的相关转换中严格识别它们，并开发一种在白色和彩色随机高斯激励下识别最小相位单神经元时间核的计算方法。与反向相关或最大似然不同，基于相关失真的标识不需要同时观察刺激响应对，而只需观察它们各自的二阶统计量即可。尽管从原理上讲，滤波器内核不一定是最小相位的，并且只能根据输出相关性唯一确定其频谱幅度，但我们证明，在实践中，该方法可以从一系列神经系统参数模型中对内核进行出色的估计。最后，我们讨论了这种方法如何潜在地使神经模型能够从各种各样的实验条件和系统及其局限性中进行估算。

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