Pulse Propagation Networks: a neural network model that uses temporal coding by action potentials

In this paper we study a model of a neural network that is fundamentally different from currently popular models. In this model we consider every action potential in the network, rather than average firing rates; this enables us to consider temporal coding by action potentials. This kind of model is not new, but we believe our results on computational ability to be new. We introduce a specific model, which we call a pulse propagation network (PPN), and consider this model from the point of view of information processing, as a dynamical system and as a computing machine. We show, in particular, that as a computing machine it can operate with real numbers and consequently it is of a class more powerful than a conventional Turing machine. In the process of this analysis, we develop a framework of concepts and techniques useful to understand and analyze these PPN.