Paradigms for Computing with Spiking Neurons
In this chapter we define for various neural coding schemes formal models of computation in networks of spiking neurons. The main results about the computational power of these models are surveyed. In particular, we compare their computational power with that of common models for artificial neural networks. Some rigorous theoretical results are presented which show that for temporal coding of inputs and outputs certain functions can be computed in a feedforward network of spiking neurons with fewer neurons than in any multi-layer perceptron (i.e., feedforward network of sigmoidal neurons). This chapter also presents a brief survey of the literature on computations in networks of spiking neurons.