2 Computing with Spiking Neurons
In the preceding chapter a number of mathematical models for spiking neurons were introduced. Spiking neurons differ in essential aspects from the familiar computational units of common neural network models, such as McCulloch-Pitts neurons or sigmoidal gates. Therefore the question arises how one can compute with spiking neurons, or with related computational units in electronic hardware whose input and output consists of trains of pulses. Furthermore the question arises how the computational power of networks of such units relates to that of common reference models, such as threshold circuits or multi-layer perceptrons. Both of these questions will be addressed in this chapter.