Does Feedback Alignment Work at Biological Timescales?
Abstract
Feedback alignment and related weight-transport-free algorithms 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. We show that learning is governed by the temporal overlap between presynaptic drive and a locally projected error signal, providing an analytic explanation for robustness to moderate timing mismatch and for failure when mismatch eliminates overlap. Our results show that in order for feedback-alignment-type algorithms to work at biological timescales, they must obey the same temporal overlap principle that applies to other biological processes like eligibility traces.
Cite
@article{arxiv.2510.18808,
title = {Does Feedback Alignment Work at Biological Timescales?},
author = {Marc Gong Bacvanski and Liu Ziyin and Tomaso Poggio},
journal= {arXiv preprint arXiv:2510.18808},
year = {2026}
}