The Synergy of Synaptic Scaling and Internal Homeostatic Plasticity

Inspired by the functionality of biological neural networks, scientists have been continuously developing various computer models, with spiking neural networks emerging as the most biologically relevant representation. To mimic the human brain more effectively, integrating the mechanisms of homeostatic plasticity, which are widespread in living neural networks, becomes crucial. Within the scope of this study, a model of the simplest spiking neural network was constructed, incorporating two mechanisms of homeostatic plasticity - internal plasticity and synaptic scaling. It was shown that the first mechanism in the model achieves stability of neuronal activity at a predetermined level. The second mechanism enables the implementation of negative feedback, leading to successful neuron learning and possessing biological plausibility.