Editing Hopfield network (section)

===Learning rules===
There are various different [[learning rule]]s that can be used to store information in the memory of the Hopfield network. It is desirable for a learning rule to have both of the following two properties:
* ''Local'': A learning rule is ''local'' if each weight is updated using information available to neurons on either side of the connection that is associated with that particular weight.
* ''Incremental'': New patterns can be learned without using information from the old patterns that have been also used for training. That is, when a new pattern is used for training, the new values for the weights only depend on the old values and on the new pattern.<ref name="storkey1991basins" />

These properties are desirable, since a learning rule satisfying them is more biologically plausible. For example, since the human brain is always learning new concepts, one can reason that human learning is incremental. A learning system that was not incremental would generally be trained only once, with a huge batch of training data.