Editing Connectionism (section)

===Memory and learning===
{{Main|Artificial neural networks|Deep learning}}
Neural networks follow two basic principles:

# Any mental state can be described as a ''n''-dimensional [[Vector (mathematics)|vector]] of numeric activation values over neural units in a network.
# Memory and learning are created by modifying the 'weights' of the connections between neural units, generally represented as an ''n''×''m'' [[Matrix (mathematics)|matrix]]. The weights are adjusted according to some [[learning rule]] or algorithm, such as [[Hebbian theory|Hebbian learning]].<ref>{{Cite journal|last1=Novo|first1=María-Luisa|last2=Alsina|first2=Ángel|last3=Marbán|first3=José-María|last4=Berciano|first4=Ainhoa|date=2017|title=Connective Intelligence for Childhood Mathematics Education|journal=Comunicar|language=es|volume=25|issue=52|pages=29–39|doi=10.3916/c52-2017-03|issn=1134-3478|doi-access=free|hdl=10272/14085|hdl-access=free}}</ref>

Most of the variety among the 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".