What is Pylyshn's critique of neural nets ?
The concept of neural nets was formulated in order to produce a model
that had a greater degree of neurosimiltuide to actual neural action
than was suggested in previously proposed information processing
models. A neural net consists of an input layer which communicates with
the output neurode by a system of interconnecting neurodes. The most
basic of these is the perceptron which consists of just two layers.
However it has been found that by incorporating hidden layers a neural
net can be trained to give feedback. This feedback serves to either
inhibit or excite the connections which in turn will decrease or
increase the likelihood of getting a correct output next time.
Pylyshn criticises neural nets for what they cannot do as well or as
easily as symbol systems. He maintains that if neural nets are just the
hardware for a symbol system then this is irrelevant information anyway
as what is necessary is ' implementation independence ' . Whilst
symbols are arbitrary and can be combined and recombined in many
different ways , neural nets stand for something as a whole and cannot
be broken down into its constituent parts , for example from a neural
net of ' the cat sat on the mat ' a neurode cannot be found for ' cat '
or ' mat ' independently. Thus they have not got the same manipulation
power and are therefore much less flexible than symbol systems are.
Pylyshn maintains therefore that neural nets are not suitable
representations for the flexibility of language and thought which are
purely propositional and therefore symbolic. Thus the association of
one chunk of information with another does not reflect the complexity
and flexibility of thought and therefore does not have the property of
"systematicity" evident in symbol systems. -- Claire Nelson py104
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