Pillow Jonathan W, Simoncelli Eero P
Gatsby Computational Neuroscience Unit, University College London, London, UK.
J Vis. 2006 Apr 28;6(4):414-28. doi: 10.1167/6.4.9.
We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses. The resulting approach has several useful properties, namely, (1) it recovers a set of linear filters sorted according to their informativeness about the neural response; (2) it is both computationally efficient and robust, allowing recovery of multiple linear filters from a data set of relatively modest size; (3) it provides an explicit "default" model of the nonlinear stage mapping the filter responses to spike rate, in the form of a ratio of Gaussians; (4) it is equivalent to maximum likelihood estimation of this default model but also converges to the correct filter estimates whenever the conditions for the consistency of STA or STC analysis are met; and (5) it can be augmented with additional constraints on the filters, such as space-time separability. We demonstrate the effectiveness of the method by applying it to simulated responses of a Hodgkin-Huxley neuron and the recorded extracellular responses of macaque retinal ganglion cells and V1 cells.
我们描述了一种信息理论框架，用于使用线性-非线性-泊松级联模型拟合神经脉冲响应。该框架统一了用于神经特征描述的脉冲触发平均（STA）和脉冲触发协方差（STC）方法，并恢复了一组线性滤波器，这些滤波器可最大化刺激与脉冲响应之间依赖于均值和方差的信息。由此产生的方法具有几个有用的特性，即：（1）它恢复了一组根据其对神经响应的信息量排序的线性滤波器；（2）它既计算高效又稳健，允许从相对较小规模的数据集中恢复多个线性滤波器；（3）它以高斯比率的形式提供了将滤波器响应映射到脉冲率的非线性阶段的显式“默认”模型；（4）它等同于该默认模型的最大似然估计，但只要满足STA或STC分析一致性的条件，也会收敛到正确的滤波器估计；（5）它可以通过对滤波器施加额外约束（如时空可分离性）进行扩充。我们通过将该方法应用于霍奇金-赫胥黎神经元的模拟响应以及猕猴视网膜神经节细胞和V1细胞的细胞外记录响应，证明了该方法的有效性。
