רשת נוירונים גדלה בעצמה לארכיטקטורה מותאמת

תקציר מקורי באנגליתarXiv:2512.12713v2 Announce Type: replace-cross Abstract: Control policies are often implemented with fixed-capacity multilayer perceptrons trained by backpropagation, which require architecture selection in advance and cannot adapt their capacity during learning. This paper introduces the Self-Motivated Growing Neural Network (SMGrNN), a gradient-trained controller whose topology evolves online through a local Structural Plasticity Module (SPM). The SPM monitors edge-wise weight update statistics over short temporal windows and uses these local signals to trigger neuron insertion and pruning, while synaptic weights are optimized by a standard gradient-based optimizer. This allows network capacity to be adjusted during learning without manual architectural tuning. SMGrNN is evaluated on co