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Hebbian-type learning
rules are attractive because of their simplicity. Although we
considered standard variants of Hebbian rules in our quest for a
visual system that can learn to represent depth information, we
rejected them because they were subject to a severe theoretical
problem. If a Class II neuron, which is activated by lateral
excitation alone, excites other neurons and causes them to become
active without bottom-up excitation, then a Hebbian-type learning rule
would cause its lateral excitatory connections to the other neurons to
strengthen, and it would cause the bottom-up inputs to the other
neurons to weaken. The other neurons would thereby tend to be
converted to Class II neurons. Thus, if the network contains Class II
neurons, then Hebbian-type rules have no way to prevent all
neurons within the same network stage from becoming Class II neurons.
If that happened, then the network stage would be divorced from actual
visual input flowing from prior stages (Figure 3b). Neuron
activations within the stage would propagate in an uncontrolled,
``hallucinatory'' manner.
Figure 3: Hallucination problem.
(a) The learning rules must be
chosen so that bottom-up image information can flow to every layer,
e.g., via connections indicated by vertical arrows. (b) Otherwise,
some layers might learn strong lateral connections at the expense of
bottom-up connections. Such layers would become disconnected from
actual visual input signals.
Clearly, that would be a failure. We sought a learning system that
would prevent hallucinatory networks from developing. The rules that
we chose were thus required to be grounded -- i.e., to keep
some neurons supplied with actual bottom-up input.
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