Introduction

In dealing with the problem of trying to describe mathematically neural tissue populations, known as neural nets, two approaches can be taken, the global and the microscopic. The global approach gives a phenomenological description of neural tissue populations. The microscopic, derived through appropriate simplifications, gives properties of the net from the properties of its constituents, the neurons, the connections and the synapses. While phenomenological theories appear to be easier to build and, overall, yield more results that are in agreement with experiments, it is impossible to build a realistic model of the brain without knowing the detailed functioning, interactions and interrelations of its constituents.

For the construction of an actual neural net machine, theories based on the properties of the fundamental constituents of the net will be more applicable to discovering the laws that govern the secrets of biological information processing. For instance, it is more relevant to simulate the activities of the brain from the underlying fundamental laws of nature. Examples taken from physics itself clarifies this point.

From a philosophical point of view it is more appealing to derive the laws of thermodynamics from the statistical behaviour of the particles that make up the system than to introduce thermodynamics as an independent branch of physics.

Analogously, it is more significant to derive the properties of superconductivity from the actual properties of the electrons and lattice than from phenomenological reasoning.