Intro & basic
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This essay was originally part of the series "The Anatomical Basis of Mind", available on this website as The Anatomical Basis of Mind (Neurophysiology & Consciousness). The purpose of that series was to learn & explain what is known about how neurophysiology results in the phenomena of mind&self. Further, to understand which brain/neuronal structures must be preserved to preserve mind&self. And still further, to be able to use that knowledge to suggest & evaluate cryonics and other preservation methods. This installment addresses the subject of computer-models of neural networks and the relevance of those models to the functioning brain. The computer field of Artificial Intelligence is a vast bottomless pit which would lead this series too far from biological reality -- and too far into speculation -- to be included.
OVERVIEW OF NEURAL NETWORKS
Discovery of the neural cell of the brain, the neuron
Crash Introduction to Artificial Neural Networks
Introduction to Neural Networks Competitive Learning and Self Organized Maps
One question which seems to puzzle many of those who encounter unsupervised learning for the first time is how can anything useful be achieved when input information is simply poured into a black box with no provision of any rules as to how this information should be categorized. If the information is sorted on the basis of how similar one input is with another, then we will have accomplished an important step in condensing the available information by developing a more compact representation. We can represent this information, and any subsequent information, in a much reduced fashion.Neural Networks
Many concepts related to the neural networks methodology are best explained if they are illustrated with applications of a specific neural network program.
Induction of pattern classifiers from data is an important area of research in machine learning which finds applications in diverse areas including automated diagnosis, bioinformatics, design of customizable information assistants, intrusion detection in computer systems, among others. Artificial neural networks, because of their potential for massive parallelism and fault and noise tolerance, offer an attractive approach to the design of trainable pattern classifiers. Constructive learning algorithms, which avoid the guesswork involved in deciding a suitable network architectures for different pattern classification problems by growing a network by recruiting neurons as needed can be effectively trained to solve complex pattern classification problems.