פתרון גרדיאנטי-בספייק-טיימינג-דפנדנטי לבעיית הלמידה בעקבות פידבק במערכות נוירונים קטנות

תקציר מקורי באנגליתarXiv:2609.08070v1 Announce Type: cross Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state. This enables an online feedback learning framework for NMCs through a gradient tunneling (GT) algorithm and the lead-lag expansion technique that derives credit assignment from local synaptic sp