Introduction

In the last few years we have learnt an enormous amount about how the immune system functions. We now have at least the outline of an immune system network theory that seems to account for much of the phenomenology (Hoffmann, 1980, 1982, Hoffmann et al. 1988). The many similarities between the immune system and the central nervous system suggested the possibility that the same kind of mathematical model could be applicable to both systems. We found that a neural network theory analogous to the immune system theory can indeed be formulated (Hoffmann, 1986). The basic variables in the immune system network theory are clone sizes; the corresponding variables in the neural network theory are the rates of firing of neurons. We need to postulate that neurons are slightly more complex than has been assumed in conventional neural network theories, namely that there can be hysteresis in the rate of firing of a neuron as the input level of the neuron is varied.

The added complexity of the hysteresis postulate is compensated by a new simplicity at the level of the network; the network can learn without any changes in the synaptic connection strengths (Hoffmann, Benson, Bree & Kinahan, 1986). Learned information is associated solely with a state vector; memory is a consequence of the fact that due to the hysteresis associated with each neuron, the system tends to stay in the region of an N-dimensional phase space to which its experiences have taken it. A network's stimulus–response behaviour is determined by its location in that space.