1. A method for emulating human cognition in electronic form, comprising: receiving information in a form of a textual or voice input in a natural language; parsing the received input into pre-determined semantic phrases based on a stored set of language rules for the natural language; defining a plurality of concept neurons that each represent a unique concept and stored in a database of concept neurons, each of the concept neurons having relational associations with other of the concept neurons through unique weighting factors, the relational associations defining the concept associated with the concept neuron, certain of the concept neurons associated with phrases representing idioms that are associated with a unique idiom concept; determining if the parsed semantic phrases define one of the unique idiom concepts associated with phrases representing that idiom and their associated concept neurons and, if so: then creating a plurality of weighted relational associations to other concept neurons stored in the database that represent in the natural language a body of concepts in an ordered list of the weighted relational associations for each item in the ordered list, the created weighted relational associations operable to create a weighted relationship as the related associations to the other concept neurons from the determined defined one of the associated concept neurons and associating the ordered list with the determined defined one of the associated concept neurons associated with one or more of the concepts associated with the parsed semantic phrases determined to define the concept; and determining if the parsed phrases determined to define the unique idiom concept associated with the phrase constitute a query and, if so, then using the weighted relational associations to make a decision to the query.
1. A method for emulating human cognition in electronic form, comprising: receiving information in a form of a textual or voice input in a natural language; parsing the received input into pre-determined semantic phrases based on a stored set of language rules for the natural language; defining a plurality of concept neurons that each represent a unique concept and stored in a database of concept neurons, each of the concept neurons having relational associations with other of the concept neurons through unique weighting factors, the relational associations defining the concept associated with the concept neuron, certain of the concept neurons associated with phrases representing idioms that are associated with a unique idiom concept; determining if the parsed semantic phrases define one of the unique idiom concepts associated with phrases representing that idiom and their associated concept neurons and, if so: then creating a plurality of weighted relational associations to other concept neurons stored in the database that represent in the natural language a body of concepts in an ordered list of the weighted relational associations for each item in the ordered list, the created weighted relational associations operable to create a weighted relationship as the related associations to the other concept neurons from the determined defined one of the associated concept neurons and associating the ordered list with the determined defined one of the associated concept neurons associated with one or more of the concepts associated with the parsed semantic phrases determined to define the concept; and determining if the parsed phrases determined to define the unique idiom concept associated with the phrase constitute a query and, if so, then using the weighted relational associations to make a decision to the query. 5. The method of claim 1 , wherein the step of if the parsed phrases define a concept comprises determining if the parsed phrase includes a triad of a speaker, the person/object spoken to, and the person, object or subject spoken of.