Instant Pattern Filtering and Discrimination in a Multilayer Network with Gaussian Distribution of the Connections

This paper was designed to build an artificial multilayer network with the purpose of studying abilities like instant pattern recognition and discrimination where no learning would be required. The relevance refers to: (1) theories about putative biological mechanisms that would support innate perception, (2) technological implementation of faster systems for detection and classification of environmental stimulus without learning. Our model was built using few paradigmatic principles of neural organization. The connections obey a Gaussian function. When the network is submitted to diverse input patterns it produces both discriminative and distributed codes in all layers. Contrasting stimulus leads to an attention-like process by salience detection. Finally, the codes always hold a half of all nodes.