Neurological researchers have recently revealed their findings about
increasingly common and sophisticated role of the sixth computer (natural
intelligence) generation namely, Artificial neural networks (ANNs). This
computer generation is relevantly applicable to implement realistic learning
models originated from interdisciplinary discipline incorporating
neuroscience, education, and cognitive sciences. Consequently, implemented
realistic models have diverse structural paradigms, in consequence with
natural characterized features of brain functioning assigned for modeling.
This paper specifically addressed an interdisciplinary research work problem
originated from quantitative evaluation of neuronal mechanism for reading
languages' preliminary phases. Briefly, it concentrates on a very interesting
and challenging issue tightly associated to quantitative learning performance
of vocabulary evaluation at early infancy of human brain, while that affected
with mother's speech. Therefore, due to prevailing concept of individual
intrinsic characterized properties of highly specialized neurons. Presented
ANN models have been closely correspond to performance of these neurons
for developing preliminary phase of reading brain in a significant way.
More specifically; presented models concerned with their important neurons'
role played in carrying out cognitive brain function's learning outcomes . In
this context, herein introduced work illustrates via ANN s simulation results :
How ensembles of highly specialized neurons could be dynamically involved
in performing the cognitive function of recognizing words' vocabulary during
early infancy development of human reading brain.
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