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Most of the variety among neural network models comes from:
* ''Interpretation of units'': Units can be interpreted as neurons or groups of neurons.
* ''Definition of activation'': Activation can be defined in a variety of ways. For example, in a [[Boltzmann machine]], the activation is interpreted as the [[probability]] of generating an action potential spike, and is determined via a [[logistic function]] on the sum of the inputs to a unit.
* ''Learning algorithm'': Different networks modify their connections differently. In general, any mathematically defined change in connection weights over time is referred to as the "learning algorithm".
 
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