Hierarchical Mixture Models in Neurological Transmission Analysis
研究了分层混合模型在分析哺乳动物中枢神经系统突触传递特征中的应用，通过贝叶斯先验分布捕捉神经递质释放位点的随机响应机制，并用随机模拟进行后验分析，为神经生理学实验提供统计推断工具。
Abstract Hierarchically structured mixture models are studied in the context of data analysis and inference on neural synaptic transmission characteristics in mammalian, and other, central nervous systems. Mixture structures arise due to uncertainties about the stochastic mechanisms governing the responses to electrochemical stimulation of individual neurotransmitter release sites at nerve junctions. Models attempt to capture such scientific features as the sensitivity of individual synaptic transmission sites to electrochemical stimuli and the extent of their electrochemical responses when stimulated. This is done via suitably structured classes of prior distributions for parameters describing these features. Such priors may be structured to permit assessment of currently topical scientific hypotheses about fundamental neural function. Posterior analysis is implemented via stochastic simulation. Several data analyses are described to illustrate the approach, with resulting neurophysiological insights in some recently generated experimental contexts. Further developments and open questions, both neurophysiological and statistical, are noted.