Adaptive sampling by information maximization

Machens, C. K. (June 2002) Adaptive sampling by information maximization. Physical Review Letters, 88 (22). ISSN 0031-9007

DOI: 10.1103/PhysRevLett.88.228104


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.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > computers > computer software
organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
CSHL Authors:
Communities: CSHL labs > Brody lab
Depositing User: Matt Covey
Date: June 2002
Date Deposited: 08 Jan 2014 16:29
Last Modified: 08 Jan 2014 16:29
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