title: Adaptive sampling by information maximization.
abstract: 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.
authors.affiliation: Innovationskolleg Theoretische Biologie, Invalidenstrasse 43, Humboldt-University Berlin, 10115 Berlin, Germany. c.machens@biologie.hu-berlin.de
authors.forename: Christian K
authors.initials: CK
authors.lastname: Machens
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D015233:Models, Statistical; D009475:Neurons, Afferent / Q000502:physiology