Embodiments of the present invention provide an artificial neural network system for feature pattern extraction and output labeling. The system comprises a first spiking neural network and a second spiking neural network. The first spiking neural network is configured to autonomously learn complex, temporally overlapping features arising in an input pattern stream. Competitive learning is implemented as spike timing dependent plasticity with lateral inhibition in the first spiking neural network. The second spiking neural network is connected with the first spiking neural network through dynamic synapses, and is trained to interpret and label the output data of the first spiking neural network. Additionally, the labeled output of the second spiking neural network is transmitted to a computing device, such as a central processing unit for post processing.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for autonomous feature extraction, the system comprising: a hierarchical arrangement of a first artificial neural network and a second artificial neural network, said first artificial neural network autonomously learns to recognize a repeating pattern in an input stream and the second artificial neural network is trained to interpret and label the repeating pattern recognized by the first artificial neural network. 2. The system of claim 1, wherein the first artificial neural network autonomously learns any repeating pattern within 3 to 7 repetitions through spike timing dependent plasticity and lateral inhibition. 3. The system of claim 1, wherein the first artificial neural network and the second artificial neural network is a single layered or a multilayered spiking neural network. 4. The system of claim 1, wherein the labeling of the repeating pattern by the second artificial neural network is carried out by mapping learned features into output labels within a predetermined knowledge domain. 5. The system of claim 1 further comprising an input unit having a network of artificial sensory neurons connected to the first artificial neural network, said input unit converts the data captured in the form of images, characters or values by a sensor array into temporally and spatially distributed spikes. 6. The system of claim 5 wherein the sensor array comprises a plurality of sensors for capturing data of interest and generating the input stream. 7. The system of claim 1 wherein the first artificial neural network and the second artificial neural network comprises a plurality of digital neuron circuits interconnected by a plurality of synapse circuits. 8. The system of claim 1, wherein the first artificial neural network autonomously learns by means of STDP and lateral inhibition to create the predetermined knowledge domain comprising of a plurality of weights representing the learned features in the input stream. 9. The system of claim 1 wherein the second artificial neural network is configured to function in a supervised manner and is trained to produce input/output maps within the predetermined knowledge domain. 10. The system of claim 1 wherein the second artificial neural network comprises a monitoring means to identify outputs that meet some predefined criteria. 11. The system of claim 1, wherein the second artificial neural network transmits the output labels to a computing device, such as a central processing unit, for post processing. 12. The system of claim 1, wherein the plurality of sensors are analog or digital sensors, such as vision sensors, digital microphones, tactile pressure sensors, chemical sensors and the sensors that are outside of human perception such as infrared, radar or ultrasound. 13. A method for autonomously extracting features in an artificial neural network system, the method comprising: feeding an input pattern stream from a sensor to a first artificial neural network; learning, autonomously, by the first artificial neural network to recognize repeating patterns in the input stream and subsequently perform feature extraction; sending spikes representing recognized patterns to a second artificial neural network from the first artificial neural network; labeling the recognized spike patterns, representing features learned by the first neural network and received from the first artificial neural network, by the second artificial neural network; and sending labeled data representing the recognized patterns to a computing device for post processing. 14. The method of claim 13, wherein the first artificial neural network and the second artificial neural network comprises a plurality of digital neuron circuits interconnected by a plurality of synapse circuits. 15. The method of claim 13, wherein the first artificial neural network and the second artificial neural network comprises either a single layer or a multilayer of digital neuron circuits. 16. The method of claim 13, wherein the first artificial neural network autonomously learns by means of STDP and lateral inhibition to create the predetermined knowledge domain comprising of a plurality of weights representing the learned features in the input stream. 17. The method of claim 13, wherein the second artificial neural network is configured to function in a supervised manner and is trained to produce input/output maps within the predetermined knowledge domain. 18. The method of claim 13, wherein the second artificial neural network comprises a monitoring means to identify outputs that meet some predefined criteria. 19. The method of claim 13, wherein the second artificial neural network transmits the output labels to a computing device, such as a central processing unit, for post processing.