Abstract: An artificial neuromorphic neural network architecture including a feedforward connectivity structure and/or a lateral connectivity structure, a method of processing input signals with an artificial neuromorphic neural network architecture, and a generalization process. An architecture takes in as inputs a set numerical representation of raw sensory data, represented by a binary set of receptive nodes whereby, following a set of algorithms and using a particular connectivity structure achieves a set of output nodes which abstracts and generalizes efficiently over the sensory data without a need for optimization and via the introduction of selective nodes. The architecture also includes a secondary layer of connectivity structure which takes in as input a set of distributed selective nodes and produces a set of collective connectivity amongst said set of selective nodes, which results into collective excitation and collective inhibition behaviors amongst the connected nodes.