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[NN] Added info about Hebbian learning to localized learning section

Signed-off-by: Jim Martens <github@2martens.de>
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2018-05-24 11:48:26 +02:00
parent 4a474d0625
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@ -455,6 +455,14 @@ is decreasing with further distance from the source. The sources are the second
environmental feedback loop in this example as they tell the network or a part of
it when to learn.
How does the actual learning happen? The weight change between two neurons
is dependent on the activation of both neurons, the learning rate and the concentration
of neuromodulators. In short Hebbian learning is employed.
\[
\Delta w_{ij} = \eta \cdot m_i \cdot a_i \cdot a_j
\]
This explanation should suffice for the general understanding of their method.
The neurons within the vicinity of these sources only update their weights
in one of the seasons. Therefore they only learn for one season and are unaffected