Brain development is characterized by changes in connections and information processing complexity. These changes inspire the training process of artificial neural network (ANN), which requires adjusting the neuron weights and biases to enhance efficiency in performing a specific task. In this work, we found affinities in the ratio of positive and negative weights in simple ANNs during training with that of excitatory and inhibitory synapses in the cortex. Additionally, we present a graphical representation of simple ANNs formed by pruning unimportant weights and aligning neurons and connections of different layers. Our findings suggest a strong relationship between the accuracy of simple neural network and graphical representation features, with graphical features at the inflection point resembling the graphical representation of the cortex.