Adaptive Sampling by Information Maximization

作     者：Christian K. Machens 

作者机构：Innovationskolleg Theoretische Biologie Invalidenstrasse 43 Humboldt-University Berlin 10115 Berlin Germany 

出 版 物：《Physical Review Letters》 (Phys Rev Lett)

年 卷 期：2002年第88卷第22期

页      面：228104-228104页

核心收录：

学科分类：07[理学] 0702[理学-物理学] 

主　　题：.assume outputs Input-Output input and output iterative algorithm test inputs input distribution 

摘      要：The investigation of input-output systems often requires a sophisticated choice of test inputs to make the best use of limited experimental time. Here we present an iterative algorithm that continuously adjusts an ensemble of test inputs on-line, subject to the data already acquired about the system under study. The algorithm focuses the input ensemble by maximizing the mutual information between input and output. We apply the algorithm to simulated neurophysiological experiments and show that it serves to extract the ensemble of stimuli that a given neural system “expects as a result of its natural history.