Weight transport through spike timing for robust local gradients
Backpropagation in deep spiking neural networks (SNNs) relies on symmetric weight connections, conflicting with biological locality constraints and hardware implementation requirements. Method: We propose spike-based alignment learning (SAL), a biologically plausible training mechanism grounded in spike-timing statistics. SAL integrates STDP, dual-mode Hebbian/anti-Hebbian plasticity, and intrinsic neuronal noise to adaptively align asymmetric feedforward–feedback weights—without requiring weight symmetry or explicit backward weight transmission—thereby recovering accurate local gradients. The model employs probabilistic spiking neurons and a hierarchical architecture inspired by cortical microcircuits. Contribution/Results: SAL significantly improves convergence accuracy toward target distributions and enables automatic alignment of feedback weights across multiple layers. Local error estimates achieve accuracy comparable to ideal backpropagation. The method satisfies key neurobiological constraints while demonstrating robustness under realistic computational conditions.