Synaptic Scaling Balances Learning in a Spiking Model of Neocortex
This addresses the challenge of maintaining stability in biologically-realistic neural models for neuroscience researchers, but it is incremental as it builds on existing STDP and scaling concepts.
The study tackled the problem of balancing learning in a spiking model of neocortex by introducing synaptic scaling, showing it is necessary to balance positive and negative changes from potentiation and atrophy while regulating activity without altering learning.
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.