Spiking Neural Networks and Temporal Binding

Introduction

Previous chapters of this book gave an overview of connectionist recruitment learning or the neuroidal network (Feldman, 1982, 1990; Valiant, 1988, 1994, 1998, 2000b, 2005) as a discrete-time algorithm. By extending recruitment into continuous-time to make it closer to the neuronal networks of the brain, this chapter puts it in the context of spiking neural networks (Hebb, 1949; Von der Malsburg, 1981, 1994; Abeles, 1991; Gerstner, 2001) and shows its similarity to temporal binding (Von der Malsburg, 1981; Singer and Gray, 1995).

The chapter is organized as follows. First, classical neural network research is reviewed to describe the theory of spiking neural network models. Then, the development of concept representation in neural networks and the binding problem is reviewed to present the relationship between recruitment learning and the temporal binding hypothesis. A historical account of these developments is also given.