Discovering Grid-Cell Models Through Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Lin Wang and Bo Yang and Jeff Orchard",
  title =        "Discovering Grid-Cell Models Through Evolutionary
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "4683--4690",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744388",
  abstract =     "One of the main tasks in neuroscience research is to
                 interpret the activity of neurons. Given some
                 neuroscientific data, such as spike trains, one tries
                 to decipher how the activity of the neurons relate to
                 the outside world and/or the behaviour of the animal.
                 The discovery of place cells and grid cells are great
                 examples - discoveries that garnered a Nobel Prize in
                 2014. However, the spatial patterns exhibited by such
                 cells are only the beginning of our understanding of
                 spatial representation in the brain. In this paper, we
                 apply an evolutionary algorithm to discover spatial
                 patterns exhibited in cells from the entorhinal cortex
                 to see (1) if we can automatically deduce an accurate
                 model for the hexagonal-grid pattern, and (2) if we can
                 discover a more general model that also incorporates
                 grid-cell-like variants that have been observed, but
                 not understood.",
  notes =        "WCCI2016",

Genetic Programming entries for Lin Wang Bo Yang Jeff Orchard