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.