1. A method of detecting a change in an input stream via sensing events to a spiking neural network for one of a control, detection and classification application using a processor apparatus, the processor apparatus comprising a special purpose processor and memory and executing a special purpose stored program for the one of the control, the detection and the classification application, the method comprising: constructing at least one spiking neural network in the memory, the at least one spiking neural network comprising at least one input neuron and at least one output neuron, each neuron having a threshold parameter; defining at least one synapse between a pair of neurons of the spiking neural network, one neuron comprising a source neuron and the other neuron comprising a destination neuron, the synapse having a parameter that specifies a propagation delay through the synapse, the synapse permitting at least two discrete events to propagate together through the synapse from the source neuron to the destination neuron; executing the stored program in the special purpose processor, the execution resulting in the simulation of the at least one spiking neural network, the simulation causing the at least one spiking neural network to produce at least one output event at an output neuron in response to said input stream; evolutionary optimization of the at least one spiking neural network comprising a crossover operation, said crossover operation comprising selection of parent neural networks; selection of two randomly chosen neurons from at least one of said selected parent neural networks; choosing a plane using locations of the selected two randomly chosen neurons of the at least one selected parent neural network; and using a signed distance from the chosen plane to a third neuron from the at least one selected parent neural network to construct two child neural networks; assigning the third neuron to the first child network if the signed distance is non-negative and otherwise to the second child network; and mutation comprising structural pruning of the at least one spiking neural network by pruning at least one of a neuron and a synapse based upon at least one of existing connections and activity.