  Learning in the brain requires complementary mechanisms: potentiation and
activity-dependent homeostatic scaling. We introduce synaptic scaling to a
biologically-realistic spiking model of neocortex which can learn changes in
oscillatory rhythms using STDP, and show that scaling is necessary to balance
both positive and negative changes in input from potentiation and atrophy. We
discuss some of the issues that arise when considering synaptic scaling in such
a model, and show that scaling regulates activity whilst allowing learning to
remain unaltered.
