A novel perceptron architecture for simulating object construction

Artificial neural networks aim to simulate the human nervous system using the interprocessing calculation methodology, but unfortunately cannot the preserve stimulus pattern. In this paper, we depend on the biological fact “information is coded within firing rate” and hence we propose an architecture for the preceptor in which the neurons´ APs (Action Potentials) are transmitted in structures that represent the stimuli patterns, and the response of connected neuron through their synapses is highly proportional to the nature of these structures. The new preceptor that uses vector in space as input and the magic dyadic matrix shows a significant enhancement in many factors.