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. Copy Right, IJAR, 2013,. All rights reserved.