dorsal/arxiv
View SchemaAdaptive sampling by information maximization
| Authors | Christian K. Machens |
|---|---|
| Categories | |
| ArXiv ID | physics/0112070 |
| URL | https://arxiv.org/abs/physics/0112070 |
| DOI | 10.1103/PhysRevLett.88.228104 |
| Journal | Phys Rev Lett, 88:228104 (2002) |
Abstract
The investigation of input-output systems often requires a sophisticated choice of test inputs to make best use of limited experimental time. Here we present an iterative algorithm that continuously adjusts an ensemble of test inputs online, 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.
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"abstract": "The investigation of input-output systems often requires a sophisticated\nchoice of test inputs to make best use of limited experimental time. Here we\npresent an iterative algorithm that continuously adjusts an ensemble of test\ninputs online, subject to the data already acquired about the system under\nstudy. The algorithm focuses the input ensemble by maximizing the mutual\ninformation between input and output. We apply the algorithm to simulated\nneurophysiological experiments and show that it serves to extract the ensemble\nof stimuli that a given neural system ``expects\u0027\u0027 as a result of its natural\nhistory.",
"arxiv_id": "physics/0112070",
"authors": [
"Christian K. Machens"
],
"categories": [
"physics.bio-ph",
"q-bio"
],
"doi": "10.1103/PhysRevLett.88.228104",
"journal_ref": "Phys Rev Lett, 88:228104 (2002)",
"title": "Adaptive sampling by information maximization",
"url": "https://arxiv.org/abs/physics/0112070"
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