Inquire about purchasing the book | Table of Contents | Annotated Bibliography | Class on Nature of Mind
These are excerpts and elaborations from my book "The Nature of Consciousness"
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Non-sequential Programming Neural networks are
fundamentally different from the sequential, Von Neumanncomputer. Information is
processed in parallel, rather than sequentially. The network can modify itself
(i.e., learn), based on its performance. Information is spread across the
network, rather than being localized in a particular storage place. The network
as a whole can still function even if a piece of the network is not
functioning. The technology of neural networks
promised to lead to a type of computer capable of learning, and, in general, of
more closely resembling our brain. The brain is a neural
network that exhibits one important property: all the changes that occur in the
connections eventually "converge" towards some kind of stable state.
For example, the connections may change every time I see a friend's face from a
different perspective, but they "converge" towards the stable state
in which I always recognize him as him. Some kind of stability is important for
memory to exist, and for any type of recognition to be performed. Neural networks must exhibit the same
property if they have to be useful for practical purposes and plausible as
models of the brain. Several different
mathematical models were proposed in the quest for the optimal neural network. The discipline of neural
networks quickly picked up steam. More and more complex machines were built.
Until in 1968 the US mathematician Marvin Minskyproved (or thought he proved)
some intrinsic limitations of neural networks. All of a sudden, research on
neural networks became unpopular and for more than a decade the discipline
languished. Back to the beginning of the chapter "Connectionism and Neural Machines" | Back to the index of all chapters |
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