Towards an understanding of intelligence

For this discussion, intelligence is limited to the scope of biological intelligence as found in carbon-based life forms. This type of intelligence is found in nature of which human beings are very good specimens. This type of intelligence is what people normally have in mind when they talk about intelligence. A standard dictionary of the English language, [126 ], defines "intelligence" as:

1. Intelligence is the quality of being intelligent or clever.
2. Intelligence is the ability to think, reason, and understand instead of doing things automatically or by instinct.

The word "intelligent" is defined by [126] as:

• A person or animal that is intelligent has the ability to think, understand and learn things quickly and well.

In [126 ], "clever" is defined as:

• Someone who is clever is intelligent and able to understand things easily or plan things well.

From the above definitions, intelligence is the ability to think, understand, learn and plan things quickly and well instead of doing things automatically or by instinct. Based on Boole's classic work [11 ], the abilities to reason and think are combined.

[11] G. Boole, The Laws of Thought. Cambridge: MacMillen, 1854.
[126] J. Sinclair, Ed., Collins Cobuild Advanced Learner's Dictionary, English Dictionary. Glasgow: Harper Collins Publishers, 2003

The RealNeuron is a mathematical model from the viewpoint of an engineer that is based on the biological neurons’ currently-known information-processing processes and structures. It is a carefully reduced model that retains the essential features of more complex models. It is computationally efficient and only adds or subtracts ion concentrations based on the states at a membrane structure’s level. Its formulation makes it possible to optimise it.

Summary of RealNeuron

Results of eye-blink RealNeuron circuit

(c) 2006 Louwrence D Erasmus

Example of a RealNeuron circuit

(c) 2006 Louwrence D Erasmus

Here is an example of a RealNeuron circuit based on the description of the eye-blink circuit of a rabbit given by R. Menzel, Neuronale Plastizitaet, Lernen und Gedaechtnis. Neurowissenschaft Vom Molekuel zur Kognition. Berlin Heidelberg New York: Springer-Verlag, 1996, pp. 485 - 518

Introduction

This blog describes an exercise in complex systems modelling and simulation implementation. The information published in this blog is copyrighted by Louwrence D Erasmus and forms part of my Ph.D. studies at the Nortwest University, Potchefstroom Campus, South Africa.

Biological brains are one of the most complex systems known to exists in the universe. They are whole universes in themselves. The topic of brain modelling and building of machines that act as if they have brains, is a fascinating topic of study for centuries. The fact that biological brains try to model themselves through the centuries is another interesting philosophical study and outside the scope of this thesis.

The emphasis is on the modelling of the fundamental electro-chemical building blocks of intelligence as observed in nature. The ways how these building blocks interact in complex structures to achieve intelligent processing units, are also investigated.

In the end, a theory or model is useless if not applied. In engineering, theories or models are implemented to solve problems. The implementation of the model derived in this thesis is in a simulator that could be used in the design of products.