1. A method for parsing the flow of natural human language to convert the flow of natural human language represented by a flow of machine recognizable language into a conceptual flow defined by relationships among a plurality of stored concept neurons in a database, wherein each concept neuron represents a concept that is given meaning by the relationships of a given one of the concept neurons to other of the concept neurons, comprising the steps of: receiving as an input the flow of machine recognizable language as a language flow that is comprised in part of a lexical structure; recognizing the lexical structure by an artificial brain operating on a processing system; determining a basic semantic grouping structure for the language flow in the lexical structure; semantically parsing the lexical structure of the basic semantic grouping; determining in the determined basic semantic grouping the main action, occurrence or state of being for the language flow in the parsed lexical structure and mapping it to a related one or more of the concept neurons; determining the semantic role of the main action, occurrence or state of being for the language flow within the parsed lexical structure of the basic semantic grouping; determining if ambiguities exists in the determined semantic role if the semantic role maps to more than one of the concept neurons; resolving the ambiguities in the semantic role in a recursive manner by applying a predetermined set of rules thereto; determining the semantic role of other portions of the parsed lexical structures relative to the semantic role of the main action, occurrence or state of being for the language flow and mapping them to concept neurons; and forming a clump neuron that is associated with the basic semantic grouping and that embodies relationships between concept neurons identified in the lexical structure in order to represent the lexical structure in a conceptual form as a sequence of concept neurons and their related roles within the lexical structures.