  The human nervous system utilizes synaptic plasticity to solve optimization
problems. Previous studies have tried to add the plasticity factor to the
training process of artificial neural networks, but most of those models
require complex external control over the network or complex novel rules. In
this manuscript, a novel nature-inspired optimization algorithm is introduced
that imitates biological neural plasticity. Furthermore, the model is tested on
three datasets and the results are compared with gradient descent optimization.
