Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Wako-shi, Hirosawa 2-1, Saitama 351-0198, Japan.
Neural Comput. 2010 Jul;22(7):1718-36. doi: 10.1162/neco.2010.04-08-766.
Analysis of correlated spike trains is a hot topic of research in computational neuroscience. A general model of probability distributions for spikes includes too many parameters to be of use in analyzing real data. Instead, we need a simple but powerful generative model for correlated spikes. We developed a class of conditional mixture models that includes a number of existing models and analyzed its capabilities and limitations. We apply the model to dynamical aspects of neuron pools. When Hebbian cell assemblies coexist in a pool of neurons, the condition is specified by these assemblies such that the probability distribution of spikes is a mixture of those of the component assemblies. The probabilities of activation of the Hebbian assemblies change dynamically. We used this model as a basis for a competitive model governing the states of assemblies.
相关尖峰序列的分析是计算神经科学研究的一个热点。尖峰的概率分布的通用模型包含太多的参数，以至于无法用于分析实际数据。相反，我们需要一个简单但强大的相关尖峰生成模型。我们开发了一类条件混合模型，其中包含许多现有模型，并分析了它们的功能和局限性。我们将该模型应用于神经元池的动力学方面。当赫布细胞集合在神经元池中共存时，条件由这些集合指定，使得尖峰的概率分布是组成集合的概率分布的混合。赫布集合的激活概率动态变化。我们使用这个模型作为一个基础，来建立一个控制集合状态的竞争模型。
