Derivation of natural stimulus feature set using a data-driven model

  • Authors:

  • Alexander G. Dimitrov;Tomas Gedeon;Brendan Mumey;Ross Snider;Zane Aldworth;Albert E. Parker;John P. Miller

  • Affiliations:

  • Center for Computational Biology, Montana State University, Bozeman, MT;Center for Computational Biology, Montana State University, Bozeman, MT;Center for Computational Biology, Montana State University, Bozeman, MT;Center for Computational Biology, Montana State University, Bozeman, MT;Center for Computational Biology, Montana State University, Bozeman, MT;Center for Computational Biology, Montana State University, Bozeman, MT;Center for Computational Biology, Montana State University, Bozeman, MT

  • Venue:
  • ICCS'03 Proceedings of the 2003 international conference on Computational science
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

A formal approach for deciphering the information contained within nerve cell ensemble activity patterns is presented. Approximations of each nerve cell's coding scheme is derived by quantizing its neural responses into a small reproduction set, and minimizing an information-based distortion function. During an experiment, the sensory stimulus world presented to the animal is modified to contain a richer set of relevant features, as those features are discovered. A dictionary of equivalence classes is derived, in which classes of stimulus features correspond to classes of spike-pattern code words. We have tested the approach on a simple insect sensory system.