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arXiv · 2510.18808

Does Feedback Alignment Work at Biological Timescales?

Marc Gong Bacvanski·Liu Ziyin·Tomaso Poggio
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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.

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Marc Gong Bacvanski, Liu Ziyin, Tomaso Poggio. 2025-10-21. Does Feedback Alignment Work at Biological Timescales?. https://arxiv.org/abs/2510.18808

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