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To model things
we have to idealize them (e.g. atoms)
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Idealization
removes complicated details that are not
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essential
for understanding the main principles
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Allows
us to apply mathematics and to make
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analogies
to other, familiar systems.
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Once
we understand the basic principles, its easy to
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add
complexity to make the model more faithful
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It is often
worth understanding models that are known to
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be wrong (but we
mustnt forget that they are wrong!)
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E.g.
neurons that communicate real values rather
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than
discrete spikes of activity.
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