Heterosinaptik Esneklige Dayanan z-D zenleyici Bir Kazanan-Her- Seyi Alir Mekanizmasi

In this study, a self-organizing Winner-Takes-All mechanism inspired by biological neural systems and based on heterosynaptic plasticity is proposed. Unlike spike-timingdependent plasticity, synaptic learning in the proposed model is governed by a calcium-based Synaptic Tagging and Capture mechanism. This enables synaptic updates to depend not only on local activity, but also on the selective binding of proteins synthesized at the cellular level. The model was tested on spiking neural networks capable of classifying three distinct patterns with high accuracy. During training, postsynaptic neurons became specialized for specific patterns, and pattern prediction was performed using a spike-based labeling strategy. The results demonstrate that biologically plausible, unsupervised learning mechanisms can effectively lead to the emergence of Winner- Takes-All-like network structures.