1. A self-organizing computing machine for mapping from a plurality of patterns contained within at least one predetermined set of provided inputs to at least one invariant perception distinguishable by a name or a label among a plurality of categories, wherein the self-organizing computing machine comprises: at least one network of at least three nodes interconnected by variable connections into at least two hierarchical node levels including at least a lower node level and a higher node level; at least one feature extractor arranged to receive the at least one predetermined set of provided inputs, to process the at least one predetermined set of provided inputs to determine at least one hierarchical set of at least two correlants commensurate with at least two hierarchical correlant levels including at least a lower correlant level and a higher correlant level, and to communicate the determined hierarchical sets of at least two correlants to the at least two distinct nodes of the at least two distinct hierarchical node levels commensurate with the at least two correlants of the at least two distinct correlant levels such that the correlants of the lower correlant level communicate to the corresponding nodes of the lower node level and that the correlants of the higher correlant level communicates to the corresponding nodes of the higher node level; and at least one output unit arranged to interface the at least one invariant perception distinguishable by a name, or a label, among the plurality of categories; wherein, the at least one node at each hierarchical node level incorporate at least one reinforcement learning sub-network combined with at least one ensemble learning sub-network; wherein, the at least one reinforcement learning sub-network has been arranged to receive the commensurate correlants of the hierarchical sets of at least two correlants, to determine a plurality of output values and to output the output values from the determined plurality of output values to the nodes of the higher node level and the nodes of the lower node level; and wherein, the at least one ensemble learning sub-network has been arranged to receive and to combine at least one output value from the at least one node of the higher node level and to receive and to combine at least one output value from the at least one node of the lower node level.