Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models
复制标题
使用广义线性模型捕获单个神经元的动态指令集
DOI：
--
复制
发表时间：
2016
影响因子：
2.9
通讯作者：
Jonathan W. Pillow
中科院分区：
文献类型：
--
作者：
Alison I. Weber;Jonathan W. Pillow
计算神经科学中的一个关键问题是找到简单、易处理的模型，这些模型仍然足够灵活，可以捕获真实的神经元的响应特性。在这里，我们研究的能力，经常性的点过程模型称为泊松广义线性模型（GLM）。这些模型由一组线性滤波器和一个点非线性定义，并且是有条件的泊松尖峰。它们具有理想的拟合统计特性，并已被广泛用于分析电生理记录的尖峰序列。然而，GLM的动态库尚未系统地与真实的神经元进行比较。在这里，我们表明，GLM可以再现一套全面的典型的神经反应行为，包括紧张和相位尖峰，爆发，尖峰频率适应，I型和II型兴奋，和两种形式的双稳态。GLM还可以捕获尖峰定时精度和可靠性的刺激依赖性变化，其模拟在真实的神经元中观察到的那些，并且可以表现出不同程度的随机性，从几乎确定性的响应到比泊松更大的可变性。这些结果表明，泊松GLM可以表现出广泛的动态尖峰行为发现在真实的神经元，使他们非常适合定性动力学以及定量统计研究的单神经元和人口的响应特性。
A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties.
登录
查看更多内容
DOI：
--
发表时间：
2008
期刊：
--
影响因子：
--
作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
通讯作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
解开光流处理神经元之间连接的功能后果
影响因子：
25
作者：
Weber F;Machens CK;Borst A
通讯作者：
Borst A
影响因子：
2.5
作者：
Truccolo, W;Eden, UT;Brown, EN
通讯作者：
Brown, EN
影响因子：
4.3
作者：
Theis L;Chagas AM;Arnstein D;Schwarz C;Bethge M
通讯作者：
Bethge M