Ana Carolina Sousa Silva  , Sergio Souto  , Euvaldo Ferreira Cabral Jr.  , Ernane Jose Xavier Costa  , 
    Using an Easy Calculable Complexity Measure to Introduce Complexity in the Artificial Neuron Model,
    Research Journal of Biological Sciences,
    Volume 2,Issue 5,
    2007,
    Pages 607-611,
    ISSN 1815-8846,
    rjbsci.2007.607.611,
    (https://makhillpublications.co/view-article.php?doi=rjbsci.2007.607.611)
    Abstract: This study introduces an approach to simulate neural complexity by changing the McCulloch and Pitts neuron model. The new approach was tested by comparing the classification performance of a multilayer perceptron with complexity measurement capability to a traditional multilayer perceptron with McCulloch and Pitts neuron model The results showed that the multilayer perceptron implemented with the complexity measurement approach achieved best classification performance  (worst score of 94%) when compared with multilayer perceptron without the complexity approach (best score of 51%) in task of classifier large time series generated by a logistic map with different generator parameter.
    Keywords: Calculable copmplexit;artificial neurm model;complexity measurement;performance;multilayer