Editing Computational neuroscience (section)

===Memory and synaptic plasticity===
{{main|Synaptic plasticity}}
Earlier models of [[memory]] are primarily based on the postulates of [[Hebbian learning]]. Biologically relevant models such as  [[Hopfield net]] have been developed to address the properties of associative (also known as "content-addressable") style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium- and [[long-term memory]], localizing in the [[hippocampus]]. 

One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable [[synapses]] are easy to train but also prone to stochastic disruption. Stable  [[synapses]] forget less easily, but they are also harder to consolidate. It is likely that computational tools will contribute greatly to our understanding of how synapses function and change in relation to external stimulus in the coming decades.