@article{MAKHILLRJBS20072510448,
    title = {Using an Easy Calculable Complexity Measure to Introduce Complexity in the Artificial Neuron Model},
    journal = {Research Journal of Biological Sciences},
    volume = {2},
    number = {5},
    pages = {607-611},
    year = {2007},
    issn = {1815-8846},
    doi = {rjbsci.2007.607.611},
    url = {https://makhillpublications.co/view-article.php?issn=1815-8846&doi=rjbsci.2007.607.611},
    author = {Ana Carolina Sousa Silva,Sergio Souto,Euvaldo Ferreira Cabral Jr. and},
    keywords = {Calculable copmplexit,artificial neurm model,complexity measurement,performance,multilayer},
    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.}
    }