A method and apparatus for constructing one of a neuroscience-inspired artificial neural network and a neural network array comprises one of a neuroscience-inspired dynamic architecture, a dynamic artificial neural network array and a neural network array of electrodes associated with neural tissue such as a brain, the method and apparatus having a special purpose display processor. The special purpose display processor outputs a display over a period of selected reference time units to demonstrate a neural pathway from, for example, one or a plurality of input neurons through intermediate destination neurons to an output neuron in three dimensional space. The displayed neural network may comprise neurons and synapses in different colors and may be utilized, for example, to show the behavior of a neural network for classifying hand-written digits between values of 0 and 9 or recognizing vertical/horizontal lines in a grid image of lines.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of visualizing the operation and structure of a neural network used for one of a control, anomaly detection and classification application, the method using a processor apparatus, the processor apparatus comprising an input data receiver, a special purpose processor, memory, and a display capable of displaying at least a two-dimensional depiction of a portion of said neural network, and executing a special purpose stored program for visualization for a given application, the computer-implemented method comprising: loading network data describing at least a portion of the structure of the neural network into said memory using the special purpose processor, said network data describing at least two neurons and a synapse, the network data describing a connection between each of the at least two neurons and the synapse, receiving neural data from the neural network via the input data receiver, portions of said neural data corresponding to each of said at least two neurons and the synapse, said neural data comprising events and associated event times, the special purpose processor computing and storing in the memory, responsive to said neural data received via the input data receiver, event data representing said neural data and the correspondence of each said portion of said neural data to one of said at least two neurons and the synapse at at least one event time, the special purpose processor using said network data and said event data to determine an at least two-dimensional depiction of a portion of said neural network representing the structure of said portion and at least one correspondence of said event data to one of the at least two neurons and the synapse, and displaying said at least two-dimensional depiction using said display, the depiction providing information about the structure of the neural network and the operation of the neural network, the operation comprising at least one event associated with one of said at least two neurons and the synapse at said at least one event time. 2. The computer-implemented method of claim 1 further comprising the special purpose processor repeating the computing and storing at a subsequent event time to display a sequence of events in at least two two-dimensional displays, the sequence of displays representing the neural network over time. 3. The computer-implemented method of claim 1 wherein the display comprises a plurality of connections between the at least two neurons and the synapse, the neurons comprising at least one input neuron and an output neuron, wherein a path of connections between the at least one input neuron and an output neuron comprises the one of the control, the anomaly detection and the classification application. 4. The computer-implemented method of claim 2 further comprising the special purpose processor calculating and the display displaying runtime in network time units via a clock of said special purpose processor. 5. The computer-implemented method of claim 2, the at least two neurons comprising at least three neurons, an input neuron, a hidden neuron and a destination neuron, the input neuron, the hidden neuron and the destination neuron being represented by spheres of the same size with color to differentiate among input, hidden and output neurons. 6. The computer-implemented method of claim 5 further comprising tracing an event in the neural network to an input neuron and displaying one of a causality path, an input/output path and an activity of the input neuron of the sequence. 7. The computer-implemented method of claim 1 wherein the application is classification and a classification comprises a recognition of an input image. 8. The computer-implemented method of claim 7 further comprising comparing a display of a recognition of a first input image received by the data receiver with a second input image received by the data receiver, the processor comparing the first and second images and recognizing the first input image as representing a similar image to the first input image. 9. The computer-implemented method of claim 1 further comprising one of rotating, zooming and translating the at least two-dimensional display in three-dimensional space of the depiction. 10. The computer-implemented method of claim 1 further comprising varying the value of a reference time unit for visualizing charge propagation in the neural network. 11. The computer-implemented method of claim 2 further comprising differentiating between one of a synapse and a neuron that has already activated in the neural network and one of a synapse and a neuron that has not yet actuated. 12. The computer-implemented method of claim 7 further comprising using a voting scheme to determine a classification of an input image. 13. The computer-implemented method of claim 1 further comprising increasing the size of one of a neuron and a synapse as an event occurs on the neuron or the synapse. 14. The computer-implemented method of claim 1 wherein the application comprises detection, the method further comprising selectively actuating responsive to receipt of command data a particular visualization comprising one of a visualization of a detection of an event and network activity leading to the event and animating a causality path. 15. The computer-implemented method of claim 2 wherein each event of the sequence of events is included in a display, the method comprising representing each event associated with a synapse as one of a circle, a polygon, and a sphere associated with the representation of the synapse. 16. A special purpose computer apparatus for visualizing the operation and structure of a neural network in one of a control, anomaly detection and classification application, the special purpose computer apparatus comprising an input data receiver, a special purpose processor, associated memory, and a display capable of displaying at least a two-dimensional depiction of a portion of said neural network, and executing a special purpose stored program for visualization for a given application, the special purpose processor for loading network data describing at least a portion of the structure of the neural network into the associated memory, said network data describing at least two neurons and a synapse, the network data describing a connection between each of the at least two neurons and the synapse, the special purpose processor for receiving neural data from the neural network via the input data receiver, portions of said neural data corresponding to each of said at least two neurons and the synapse, said neural data comprising events and associated event times, the special purpose processor for computing and storing in the memory, responsive to said neural data received via the input data receiver, event data representing said neural data and the correspondence of each said portion of said neural data to one of said at least two neurons and the synapse at at least one event time, the special purpose processor for using said network data and said event data to determine an at least two-dimensional depiction of a portion of said neural network representing the structure of said portion and at least one correspondence of said event data to one of the at least two neurons and the synapse, and the display, responsive to the special purpose processor, for displaying said at least two-dimensional depiction using said display, the depiction providing information about the structure of the neural network and the operation of the neural network, the operation comprising at least one event associated with one of said at least two neurons and the synapse at said at least one event time. 17. The computer apparatus of claim 16 further comprising the special purpose processor repeating the computing and storing at a subsequent event time to display a sequence of events in at least two two-dimensional displays, the sequence representing the neural network over time. 18. The computer apparatus of claim 16, the display comprising a plurality of connections between the at least two neurons and the synapse, the neurons comprising an input neuron and an output neuron, wherein a path of connections between the at least one input neuron and an output neuron comprises the one of a control, an anomaly detection and a classification application. 19. The computer apparatus of claim 16, the special purpose processor for calculating and the display for displaying runtime in network time units via a clock of said special purpose processor. 20. The computer apparatus of claim 16, the at least two neurons comprising an input neuron, a hidden neuron and a destination neuron, the display representing the at least two neurons and the synapse by spheres of the same size with color to differentiate among input, hidden and destination neurons. 21. The computer apparatus of claim 18 further comprising the processor tracing an event in the neural network to an input neuron and displaying one of an input/output, a causality path and a path based on activity in a depiction of the events from an input neuron to an output neuron. 22. The computer apparatus of claim 17 wherein the application is classification and the special purpose processor comprises a recognition of an input image. 23. The computer apparatus of claim 22 further comprising the special purpose processor for comparing a stored display of a recognition of a first input image with a second stored display of a second image, the special purpose processor for recognizing the first input image as the second image. 24. The computer apparatus of claim 16 further comprising the special purpose processor for actuating the display for rotating, zooming and translating the depiction in three-dimensional space. 25. The computer apparatus of claim 17 comprising the special purpose processor varying the value of a reference time unit for visualizing the sequence of events in the neural network. 26. The computer apparatus of claim 17 further comprising the processor actuating the display to differentiate between one of a synapse and a neuron that has already activated in the neural network and one of a synapse and a neuron that has not yet actuated.