Adaptive sampling by information maximization.

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.

• Publication year

  2001

• Venue

  Physical Review Letters

• Publication date

  2001-12-20

• Fields of study

  Biology, Medicine, Physics, Computer Science

• Identifiers
  DOI 10.1103/PhysRevLett.88.228104arXiv physics/0112070PMID 12059456
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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