could be modeled using a non-biological or logical
mechanism.
The McCulloch-Pitts neuron is a mathematical
model
of a biological neuron,
which weighs and sums its inputs
and produces an output if its threshold has been reached.
Neural processing
The idea is to connect McCulloch-Pitts neurons
in a network, called an artificial neural network.
Then you feed its various inputs with sample
data
and train the network by adjusting the weighting factors
until you get the desired outputs.
This model led to theories of automata,
cybernetics,
cognitive modeling, and artificial intelligence.
Natural
intelligence
One definition of intelligence is
mental processing that a computer can not copy,
so that “artificial intelligence” is an
oxymoron.
If we were to commit to that definition,
many people would not qualify,
because they decide nothing that a computer couldn’t.
Yet we think that computers cannot have
intuition,
cannot make leaps of thought to discover things,
and cannot be sane or insane.
Whether informed by patterns of neuroses or
pheromones,
we feel that only machines cannot suffer,
and that any animal that survives is intelligent or lucky.
McCulloch, a neurophysiologist, collaborated with Pitts, a
logician who was homeless, having invited Pitts to live with his
family. They called their model threshold logic. Each
McCulloch-Pitts neuron must receive a sum of inputs over a
threshold value before it emits its output, just as a biological
neuron.
McCulloch, a neurophysiologist, collaborated with Pitts, a logician who was homeless, having invited Pitts to live with his family. They called their model threshold logic. Each McCulloch-Pitts neuron must receive a sum of inputs over a threshold value before it emits its output, just as a biological neuron.
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