An Introduction to Neural Networks
Let us first recap the most important features of the neural networks
found in the brain. Firstly the brain contains many billions of very special kinds of cell
- these are the nerve cells or neurons. These cells are organized into a very complicated
intercommunicating network. Typically each neuron is physically connected to tens of
thousands of others. Using these connections neurons can pass electrical signals between
each other. These connections are not merely on or off - the connections
have varying strength which allows the influence of a given neuron on one of its
neighbors to be either very strong, very weak (perhaps even no influence) or anything in
between. Furthermore, many aspects of brain function, particularly the learning process,
are closely associated with the adjustment of these connection strengths. Brain activity
is then represented by particular patterns of firing activity amongst this network of
neurons. It is this simultaneous cooperative behavior of very many simple processing units
which is at the root of the enormous sophistication and computational power of the brain.
Artificial neural networks are computers whose architecture is modeled after the
brain. They typically consist of many hundreds of simple processing units which are wired
together in a complex communication network. Each unit or node is a simplified
model of a real neuron which fires (sends off a new signal) if it receives a
sufficiently strong input signal from the other nodes to which it is connected. The
strength of these connections may be varied in order for the network to perform different
tasks corresponding to different patterns of node firing activity. This structure is very
different from traditional computers.
The traditional computers that we deal with every day have changed very little since
their beginnings in the 1940's. While there have been very significant advances in the
speed and size of the silicon-based transistors, which form their basic elements - the hardware,
the overall design or architecture has not changed significantly. They still
consist of a central processing unit or CPU which executes a rigid set of rules
(the program or software) sequentially, reading and writing data from a
separate unit - the memory. All the "intelligence" of the machine resides
in this set of rules - which are supplied by the human programmer. The usefulness of the
computer lies in its vast speed at executing those rules - it is a superb machine but not
a mind.
Neural networks are very different - they are composed of many rather feeble processing
units which are connected into a network. Their computational power depends on working
together on any task - this is sometimes termed parallel processing. There is no
central CPU following a logical sequence of rules - indeed there is no set of rules or
program. Computation is related to a dynamic process of node firings. This structure then
is much closer to the physical workings of the brain and leads to a new type of computer
that is rather good at a range of complex tasks.
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