Kevin Max on Muck Rack

Kevin Max

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As seen in: 1859 Oregon's Magazine, 1889 Washington's Magazine, Nature, PLOS, Yahoo Finance, Barron's, Money, WatchUSeek.com, BuiltWorlds
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Articles

Spike-based alignment learning solves the weight transport problem

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By Andreas Baumbach, Akos Kungl, Kevin Max, Mihai Petrovici, Timo Gierlich
| Nature Verified
Abstract Learning algorithms are often subject to symmetry constraints that are difficult to reconcile with local computation in physical neuronal networks. For example, contrastive Hebbian learning typically assumes symmetric connectivity, while error backpropagation requires knowledge of the forward weights in the backward pass. To solve this weight transport problem, we introduce spike-based alignment learning (SAL), a synapse-local learning rule that harnesses noise for weight alignment.
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‘Backpropagation and the brain’ realized in cortical error neuron microcircuits

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By Kevin Max, Ismael Jaras, Arno Granier
| PLOS
Citation: Max K, Jaras I, Granier A, Wilmes KA, Petrovici MA (2026) ‘Backpropagation and the brain’ realized in cortical error neuron microcircuits. PLoS Comput Biol 22(4): e1014164. https://doi.org/10.1371/journal.pcbi.1014164 Editor: Roman Bauer, University of Surrey, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND Received: September 8, 2025; Accepted: March 25, 2026; Published: April 17, 2026 Copyright: © 2026 Max et al.
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Backpropagation through space, time and the brain - Nature Communications

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By Paul Haider, Federico Sierra Benítez, Kevin Max, Laura Kriener
| Nature Verified
Abstract How physical neuronal networks, bound by spatio-temporal locality constraints, can perform efficient credit assignment, remains an intriguing question. Both backward- and forward-propagation algorithms rely on assumptions that violate this locality in various ways. We introduce Generalized Latent Equilibrium (GLE), a framework for fully local spatio-temporal credit assignment in physical, dynamical neuronal networks.
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