Neural networks with learning and expression capability

An artificial neural network comprising:

• (a) a plurality of neurons,
• (b) each of the plurality of neurons being a processor with memory and being in an array;
• (c) the plurality of neurons comprising a plurality of elemental neurons and a plurality of structural neurons;
• (d) all elemental and structural neurons being configured to be associated with others of the elemental and structural neurons via active connections;
• (e) each elemental neuron being configured to:
• (i) represent a unique value able to be input into the artificial neural network system, the unique value being one of:
• a stimulus, an event, events, a sequence in a pattern or sequence of events;
• and
• (ii) express that unique value as an output;
• and
• (f) each structural neuron being configured to receive input from a pair of neurons with which it is associating, the pair of neurons being selected from the group consisting of:
• both elemental neurons, both structural neurons, one structural and one elemental neuron, and one elemental neuron and one structural neuron.

An artificial neural network comprising:

• (a) a plurality of neurons,
• (b) each of the plurality of neurons being a processor with memory and being in an array;
• (c) the plurality of neurons comprising a plurality of elemental neurons and a plurality of structural neurons;
• (d) all elemental and structural neurons being configured to be associated with others of the elemental and structural neurons via active connections;
• (e) each elemental neuron being configured to:
• (i) represent a unique value able to be input into the artificial neural network system, the unique value being at least one selected from the group consisting of:
• a stimulus, an event, events, a sequence in a pattern, a sequence of events, an elemental stimulus, a defined elemental pattern, a defined elemental data element, a basic input stimulus, and an output stimulus of information being processed;
• and
• (ii) express that unique value as an output;
• (f) all of the plurality of structural neurons being able to be expressed in terms of the elemental neurons from which they were derived or represent.

An artificial neural network comprising:

• (a) a plurality of neurons,
• (b) each of the plurality of neurons being a processor with memory and being a node in an array;
• (c) the plurality of neurons comprising a plurality of elemental neurons and a plurality of structural neurons;
• (d) all elemental and structural neurons being configured to be associated with others of the elemental and structural neurons via connections;
• (e) the artificial neural network being bi-directional and being able to operate in a forward mode where structural neurons are created from input events from the elemental neurons, and in a reverse mode where input events are expressed by the elemental neurons.

A method for creating an association of neurons in an artificial neural network having a plurality of neurons, one of the plurality of neurons being an initiating neuron, another of the plurality of neurons being an associated neuron operatively connected with the initiating neuron, and a further neuron of the plurality of neurons being an associating neuron operatively connected with the initiating neuron and the associated neuron;

• the method comprising:
• activating or producing an output from the initiating neuron to potentiate the associating neuron;
• and
• activating or producing an output from the associated neuron to potentiate and activate the associating neuron, the associating neuron then being activated and able to produce an output;
• the associating neuron representing the sum of what is represented by the initiating neuron and the associated neuron, and once the associating neuron represents a result, the result need not be created in another neuron.

A method of constructing an artificial neural network comprising a plurality of neurons, the plurality of neurons comprising a plurality of elemental neurons and a plurality of structural neurons, the method comprising:

• defining unique events the elemental neurons will represent;
• creating a required number of elemental neurons for the total number of unique values to be represented for all defined events;
• the unique value being at least one selected from the group consisting of:
• a stimulus, an event, events, a sequence in a pattern, a sequence of events, an elemental stimulus, a defined elemental pattern, a defined elemental data element, a basic input stimulus, and an output stimulus of information being processed;
• the plurality of elemental neurons receiving all input to the artificial neural network, all output from the artificial neural network being from the plurality of elemental neurons;
• creating the plurality of structural neurons, each of the structural neurons being created by the association of a pair of the plurality of neurons;
• each of the plurality of structural neurons being configured to produce an output on activation by the pair of neurons, the pair of neurons comprising an initiating neuron and an associated neuron;
• the association of the plurality of neurons being based on proximal characteristics;
• and
• each of the plurality of structural neurons being configured to express the pair of neurons.