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    The case for backpropagation as a biologically plausible learning rule

    A researcher says searches across thousands of learning rules failed to outperform backpropagation on ImageNet, despite hopes that their approach could improve on it.

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    1 Source, 14d ago, first seen 14d ago

    TLDR

    A computational neuroscience researcher says their work convinced them that backpropagation is biologically plausible and can be implemented through vector error feedback. They say efforts using a library of learning rules from their ICML 2020 paper failed to improve on backpropagation on ImageNet, despite searches across thousands of rules on TPUs. The researcher also cites Francioni et al.’s 2025 Nature paper as confirmation of vector-error feedback.

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    1 Source, first seen 14d ago

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    1 Source

    @aran_nayebiYup! The big lesson for me from doing this work was to convince myself that backprop *is* biologically plausible, and can be suitably implemented via vector error feedback. Prior to that point, I thought maybe the "secret sauce" of the brain could be the learning rule (rather than architecture or loss fct), but this is clearly not true since BP upper bounds these approximations. We had hoped that approximating the Hessian with our library of learning rules in our ICML '20 paper above could lead to improvement over BP on ImageNet, but this wasn't the case either, even despite doing large-scale searches across thousands of learning rules on TPUs. And while it was no longer surprising to us computational neuroscientists/NeuroAI people, it was finally nice to see confirmation of vector-error feedback in Francioni et al. Nature 2025:

    1 Source

    @aran_nayebiYup! The big lesson for me from doing this work was to convince myself that backprop *is* biologically plausible, and can be suitably implemented via vector error feedback. Prior to that point, I thought maybe the "secret sauce" of the brain could be the learning rule (rather than architecture or loss fct), but this is clearly not true since BP upper bounds these approximations. We had hoped that approximating the Hessian with our library of learning rules in our ICML '20 paper above could lead to improvement over BP on ImageNet, but this wasn't the case either, even despite doing large-scale searches across thousands of learning rules on TPUs. And while it was no longer surprising to us computational neuroscientists/NeuroAI people, it was finally nice to see confirmation of vector-error feedback in Francioni et al. Nature 2025: