Adaptive Sampling by Information Maximization

Christian K. Machens
Phys. Rev. Lett. 88, 228104 – Published 20 May 2002
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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.

  • Received 4 January 2002

DOI:https://doi.org/10.1103/PhysRevLett.88.228104

©2002 American Physical Society

Authors & Affiliations

Christian K. Machens*

  • Innovationskolleg Theoretische Biologie, Invalidenstrasse 43, Humboldt-University Berlin, 10115 Berlin, Germany

  • *Present address: Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724. Electronic address: c.machens@biologie.hu-berlin.de

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Vol. 88, Iss. 22 — 3 June 2002

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