A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia

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@Article{Floares2008379,
  author =       "Alexandru George Floares",
  title =        "A reverse engineering algorithm for neural networks,
                 applied to the subthalamopallidal network of basal
                 ganglia",
  journal =      "Neural Networks",
  volume =       "21",
  number =       "2-3",
  pages =        "379--386",
  year =         "2008",
  note =         "Advances in Neural Networks Research: IJCNN '07, 2007
                 International Joint Conference on Neural Networks IJCNN
                 '07",
  ISSN =         "0893-6080",
  DOI =          "doi:10.1016/j.neunet.2007.12.017",
  URL =          "http://www.sciencedirect.com/science/article/B6T08-4RDR1B6-1/2/5aae1d094dbe3fd190fbb3fe9acebe63",
  keywords =     "genetic algorithms, genetic programming, Neural
                 networks, Reverse engineering algorithm, Linear genetic
                 programming, Systems of ordinary differential
                 equations, Basal ganglia, Discovery science approach",
  abstract =     "Modeling neural networks with ordinary differential
                 equations systems is a sensible approach, but also very
                 difficult. This paper describes a new algorithm based
                 on linear genetic programming which can be used to
                 reverse engineer neural networks. The RODES algorithm
                 automatically discovers the structure of the network,
                 including neural connections, their signs and
                 strengths, estimates its parameters, and can even be
                 used to identify the biophysical mechanisms involved.
                 The algorithm is tested on simulated time series data,
                 generated using a realistic model of the
                 subthalamopallidal network of basal ganglia. The
                 resulting ODE system is highly accurate, and results
                 are obtained in a matter of minutes. This is because
                 the problem of reverse engineering a system of coupled
                 differential equations is reduced to one of reverse
                 engineering individual algebraic equations. The
                 algorithm allows the incorporation of common domain
                 knowledge to restrict the solution space. To our
                 knowledge, this is the first time a realistic reverse
                 engineering algorithm based on linear genetic
                 programming has been applied to neural networks.",
}

Genetic Programming entries for Alexandru Floares

Citations