[2510.18808] Does Feedback Alignment Work at Biological Timescales?

Computer Science > Machine Learning arXiv:2510.18808 (cs) [Submitted on 21 Oct 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Does Feedback Alignment Work at Biological Timescales? Authors:Marc Gong Bacvanski, Liu Ziyin, Tomaso Poggio View a PDF of the paper titled Does Feedback Alignment Work at Biological Timescales?, by Marc Gong Bacvanski and 2 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2510.18808 [cs.LG]   (or arXiv:2510.1...