Introduction

Supervised learning is a type of network learning and therefore a type of network learning rule that can lead either to dysregulation or to well-regulation of the infornet. Supervised learning works according to the back-propagation rule, where the connections between nodes in the network are strengthened or weakened (starting from the output and working backwards) according to feedback provided by the supervisor. In the biological network system that is the infornet, the genome is the supervisor. Supervised learning therefore provides a mechanism that links the genome (the repository of long-term information acquired from the parents) with the infornet (the repository of information acquired during the organism's life).

In network systems, complexity arises out of simplicity. According to infornet theory, there is one simple rule that normally leads to more effective self-regulation but under certain circumstances leads to dysregulation. This rule is called the compensation rule. This chapter shows how this single rule leads, under different circumstances, to the prediction of repetitive strain injury, attention-deficit/hyperactivity disorder, chronic fatigue syndrome, depression and asthma

The compensation rule

Let us start from the assumption, described inChapter 4, that the genome specifies patterns and the body learns to achieve those patterns. As before, we suppose that the ideal pattern is that A leads to B. But now let us suppose that the pattern of A leads to B is interrupted or attenuated by the effect of C (Figure 6.1).