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Neural Netw. 2009 Jul-Aug;22(5-6):623-32. doi: 10.1016/j.neunet.2009.06.038. Epub 2009 Jul 2.

Integrated feature and parameter optimization for an evolving spiking neural network: exploring heterogeneous probabilistic models.

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Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand.


This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

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