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