Introduction

This chapter brings together two ideas that were developed in previous chapters. The first of these is that of dysregulation. When the infornet is dysregulated it produces an inappropriate output to the inputs from the current situation. The second idea is that of learning in networks. There are two types of learning rule that are relevant to the infornet: associative learning and supervised learning. This chapter focuses on the origin of health and disease from the perspective of associative learning. The impact of supervised learning is discussed in the next chapter.

Here is a brief summary of some of the points made in earlier chapters. It is a requirement of living organisms that they constantly self-organise to achieve better self-regulation (see Chapter 1). Self-organisation is achieved through the application of network learning rules (Chapter 4). Under certain circumstances these self-organisational rules lead to poorer self-regulation, i.e., infornet dysregulation (Chapters 2 and 3). Infornet dysregulation can be defined as follows:

Associative learning

Associative learning is based on a simple network learning rule. The rule is to strengthen the connection between units of a network that are simultaneously activated (Chapter 4). The consequence of this rule is that events that are contiguous in time tend to become associated.