16. An apparatus for learning using a spiking neural network, comprising: (a) means for delaying an input spike in an artificial neuron according to a current delay associated with an input to the artificial neuron, wherein the input spike occurs at an input spike time relative to a reference time for the artificial neuron; (b) means for emitting an output spike from the artificial neuron based, at least in part, on the delayed input spike; (c) means for determining an actual time difference between the emission of the output spike from the artificial neuron and the reference time for the artificial neuron; and (d) means for adjusting the current delay associated with the input based on a difference between a target time difference and the actual time difference, the current delay, and an input spike time for the input spike.
16. An apparatus for learning using a spiking neural network, comprising: (a) means for delaying an input spike in an artificial neuron according to a current delay associated with an input to the artificial neuron, wherein the input spike occurs at an input spike time relative to a reference time for the artificial neuron; (b) means for emitting an output spike from the artificial neuron based, at least in part, on the delayed input spike; (c) means for determining an actual time difference between the emission of the output spike from the artificial neuron and the reference time for the artificial neuron; and (d) means for adjusting the current delay associated with the input based on a difference between a target time difference and the actual time difference, the current delay, and an input spike time for the input spike. 22. The apparatus of claim 16 , wherein the artificial neuron has an exponentially growing membrane potential and continues to depolarize in an absence of an inhibitory input and wherein an excitatory input causes the artificial neuron to fire sooner than the artificial neuron would fire without the excitatory input.