We have seen that artificial neural networks based on simple models for neurons and
their connections can be very successful both in simulating the memory storing and recall
process (the Hopfield network) and for pattern-based decision making and learning (the
Perceptron model). Both of these networks have already found wide application outside of
neuroscience - in fields as diverse as signal processing, recognition and synthesis of
speech, financial forecasting and modelling, and medical diagnosis.
In general, neural networks provide good solutions to problems with the following
features:
- The problem makes use of `noisy' data
- Fast processing may be required. We may not need the most perfect solution to the
problem. We might just want a reasonably good one quickly.
- There are no simple rules for solving the problem - all we have are a set of sample
solutions. The network can `trained' on these so that it produces good responses to
similar new cases.
There are two principal problems with the use of these networks. The first is that
there is no current understanding of how big (how many nodes and connections) a network
must be in order to tackle a problem of some given complexity (the exception to this being
the Hopfield network). The second disadvantage with these networks can be the very long
times sometimes needed to teach the network the appropriate responses - these networks
learn in a supervised way - input data is fed many times to the network and the
connections adjusted so as to try to achieve a target output. This "programming"
stage can mean that a given pattern must be presented to the network thousands of times.
A general statement about both these networks can also be made - we get out pretty much
what we put in - we decide how the network is to respond and adapt during learning. This
is clearly rather different from the brain which "by itself" is able to set up
connections between neurons in order to accomplish certain functions - it is said to
exhibit self-organization. It is difficult to imagine how the complexity of human thought
and consciousness can emerge from anything other than a self-organizing system. We shall
discuss these wider issues concerning artificial intelligence later.
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