Does Feedback Alignment Work at Biological Timescales?

Feedback alignment and related weight-transport–free algorithms of error propagation are often proposed as biologically plausible alternatives to backpropagation, yet they are typically formulated in discrete phases with implicitly synchronized forward and error signals. We develop a continuous-time model of feedback-alignment–type learning in which neural activities and synaptic weights evolve together under coupled first-order dynamics with distinct propagation, plasticity, and decay time constants. Our analysis shows how temporal overlap between presynaptic drive and locally projected error signals governs robustness to timing mismatch. For these two-signal rules, slow weight dynamics cannot bridge feedback delays after stimulus-specific activity has disappeared. We demonstrate learning with millisecond neural dynamics and stimulus presentations lasting tens to hundreds of milliseconds, with simultaneous inference and plasticity.