Brief History of Neural Networks
The study of the human brain dates back thousands of years. But
it has only been with the dawn of modern day electronics that man
has begun to try and emulate the human brain and its thinking processes.
The modern era of neural network research is credited with the work
done by neuro-physiologist, Warren McCulloch and young mathematical
prodigy Walter Pitts in 1943. McCulloch had spent 20 years of life
thinking about the "event" in the nervous system that allowed to
us to think, feel, etc. It was only until the two joined forces
that they wrote a paper on how neurons might work, and they designed
and built a primitive artificial neural network using simple electric
circuits. They are credited with the McCulloch-Pitts Theory of Formal
Neural Networks. (Haykin, 1994, pg: 36) (http://www.helsinki.fi)
The next major development in neural network technology arrived
in 1949 with a book, "The Organization of Behavior" written by Donald
Hebb. The book supported and further reinforced McCulloch-Pitts's
theory about neurons and how they work. A major point bought forward
in the book described how neural pathways are strengthened each
time they were used. As we shall see, this is true of neural networks,
specifically in training a network. (Haykin, 1994, pg: 37)(http://www.dacs.dtic.mil)
During the 1950's traditional computing began, and as it did, it
left research into neural networks in the dark. However certain
individuals continued research into neural networks. In 1954 Marvin
Minsky wrote a doctorate thesis, "Theory of Neural-Analog Reinforcement
Systems and its Application to the Brain-Model Problem", which was
concerned with the research into neural networks. He also published
a scientific paper entitled, "Steps Towards Artificial Intelligence"
which was one of the first papers to discuss AI in detail. The paper
also contained a large section on what nowadays is known as neural
networks. In 1956 the Dartmouth Summer Research Project on Artificial
Intelligence began researching AI, what was to be the primitive
beginnings of neural network research. (http://www.dacs.dtic.mil)
Years later, John von Neumann thought of imitating simplistic neuron
functions by using telegraph relays or vacuum tubes. This led to
the invention of the von Neumann machine. About 15 years after the
publication of McCulloch and Pitt's pioneer paper, a new approach
to the area of neural network research was introduced. In 1958 Frank
Rosenblatt, a neuro-biologist at Cornell University began working
on the Perceptron. The perceptron was the first "practical" artificial
neural network. It was built using the somewhat primitive and "ancient"
hardware of that time. The perceptron is based on research done
on a fly's eye. The processing which tells a fly to flee when danger
is near is done in the eye. One major downfall of the perceptron
was that it had limited capabilities and this was proven by Marvin
Minsky and Seymour Papert's book of 1969 entitled, "Perceptrons".
(http://www.dacs.dtic.mil) (Masters, 1993, pg: 4-6)
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