A method for creating a dynamic neural function library that relates to Artificial Intelligence systems and devices is provided. Within a dynamic neural network (artificial intelligent device), a plurality of control values are autonomously generated during a learning process and thus stored in synaptic registers of the artificial intelligent device that represent a training model of a task or a function learned by the artificial intelligent device. Control Values include, but are not limited to, values that indicate the neurotransmitter level that is present in the synapse, the neurotransmitter type, the connectome, the neuromodulator sensitivity, and other synaptic, dendric delay and axonal delay parameters. These values form collectively a training model. Training models are stored in the dynamic neural function library of the artificial intelligent device. The artificial intelligent device copies the function library to an electronic data processing device memory that is reusable to train another artificial intelligent device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of creating a reusable dynamic neural function library for use in artificial intelligence, the method comprising the steps of: sending one or more input pulses in form of stimuli to a first artificial intelligent device, where the first artificial intelligent device includes a hardware network of reconfigurable artificial neurons and synapses; learning at least one task or a function autonomously from the input pulse, by the first artificial intelligent device; generating and storing a set of control values, representing one learned function, in synaptic registers of the first artificial intelligent device; altering and updating the control values in synaptic registers, based on the time interval and the intensity of the input pulses for autonomous learning of the functions, thereby creating the function that stores sets of Control Values, at the first artificial intelligent device; and transferring and storing the function in a function library, together with other functions derived from a plurality of artificial intelligent devices, allowing a second artificial intelligent device to reuse one or more of the functions learned by the first artificial intelligent devices. 2. The method of claim 1 wherein the neural function library is transferable to a storage medium. 3. The method of claim 1 further comprising constructing at least one complex function by combining and continuous autonomous learning of the functions; and updating the Control Values in the synaptic registers that represent previously uploaded functions. 4. The method of claim 1 wherein the stored Control Values determine behavior of a processing node of the first artificial intelligent device. 5. The method of claim 4 wherein the behavior of the processing node is an output pulse, or a sequence of output pulses, that represent the integrated time relationship and spatial distribution between input pulses, integrated according to stored values that represent the learned timing sequence and relative positioning of previously received pulses. 6. The method of claim 1, wherein the first artificial intelligent device autonomously develops a relational association between the learned functions. 7. The method of claim 1, wherein the learned functions stored in the reusable dynamic neural function library further comprises a set of first functions; a set of second functions; and a set of third functions. 8. The method of claim 7, wherein the set of first function is generated by exposing the first artificial intelligent device to the input pulses in order to prime the first artificial intelligent device to respond to a defined stimuli; and further autonomous learning of the first artificial intelligent device from a plurality of samples of input pulses to perform a desired behavior. 9. A method of claim 7, wherein the set of second function comprises relational association of multiple sets of first functions. 10. A method of claim 7, wherein the set of third function comprises relational association of multiple sets of second functions.