Introduction

The previous chapters have used the terms network and infornet without any formal attempt to describe the difference between network or parallel processing systems on the one hand versus sequential processing or non-network systems on the other. The aim of this chapter is to describe the emergent properties of networks, particularly as they relate to the infornet, and to provide some detail about how those emergent properties arise as a function of network structure. The chapter expands on ideas introduced in earlier chapters. It shows how networks solve problems, and therefore how the infornet solves the problem of how best to adjust the reference criteria of homeostatic loops. The chapter provides an introduction to the emergent properties of control systems and describes the network learning rules that are the basis for infornet dysregulation.

A very brief history of networks

In the 1940s, a small number of authors (e.g., Hebb, 1949; McCulloch and Pitts, 1943) presented a theory that could be used to explain the learning phenomena that psychologists were then investigating. They pointed out that the brain was a network of neurons and that it was possible to explain learning phenomena in animals if the brain, as a network structure, followed a simple rule. This simple rule explained why classical conditioning took place and why habits were formed. The rule was subsequently called the association rule or Hebbian rule. It sometimes happens that important theoretical suggestions are ahead of their time and so have little immediate impact.