Zhang Xuejuan, You Gongqiang, Chen Tianping, Feng Jianfeng
Mathematical Department, Zhejiang Normal University, Jinhua, PR China.
Neural Comput. 2009 Nov;21(11):3079-105. doi: 10.1162/neco.2009.06-08-807.
An expression for the probability distribution of the interspike interval of a leaky integrate-and-fire (LIF) model neuron is rigorously derived, based on recent theoretical developments in the theory of stochastic processes. This enables us to find for the first time a way of developing maximum likelihood estimates (MLE) of the input information (e.g., afferent rate and variance) for an LIF neuron from a set of recorded spike trains. Dynamic inputs to pools of LIF neurons both with and without interactions are efficiently and reliably decoded by applying the MLE, even within time windows as short as 25 msec.
基于随机过程理论的最新发展，严格推导了漏电积分发放（LIF）模型神经元的峰间期概率分布表达式。这使我们首次找到了一种从一组记录的尖峰序列中为LIF神经元开发输入信息（例如，传入速率和方差）的最大似然估计（MLE）的方法。通过应用MLE，即使在短至25毫秒的时间窗口内，对有交互作用和无交互作用的LIF神经元池的动态输入也能得到有效且可靠的解码。
