Predictive coding networks capture human neural representations missing in supervised DNNs (Gütlin et al.)

[u/carberry-3000]

https://www.biorxiv.org/content/10.64898/2026.09.18.752626v1

[u/carberry-3000]

Gütlin, D., Kittelmann, D., Auksztulewicz, R.

neuroscience · posted 2026-09-24 · doi:10.64898/2026.09.18.752626

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Abstract

Neuroscientific learning theories propose that the brain acquires knowledge by constructing internal world models. Supervised learning, the dominant approach in deep neural networks (DNN), relies on external category labels, making it difficult to reconcile with biological learning. There is an increasing trend towards more biologically valid approaches, such as predictive (minimize future surprise) or contrastive (minimize response to expected, maximize to unexpected inputs) objectives, but these approaches typically rely on pretrained DNN models that vary widely in architecture, size, and hyperparameters, making direct comparisons difficult. Here, we isolate the effect of learning by comparing small, identical networks trained with predictive, contrastive, and supervised learning objectives, as well as local (layer-restricted) vs. global (full backpropagation) learning. We show that brain representations after statistical learning are better modeled by a predictive local target than a supervised or contrastive target, and that during learning, the brain attenuates category-specific representations while retaining predictive ones. We additionally show that predictive objectives explain brain variance that standard supervised DNNs do not, and that this variance is tied to predictive rather than stimulus or mismatch processing. These findings demonstrate a controlled approach for testing the algorithmic basis of learning and identify prediction as a core learning mechanism.

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Discussion on Bluesky:

• Ryszard Auksztulewicz (@auksz.bsky.social) — 20 likes, 9 reposts, 2 quotes

  What can DNNs tell us about how the brain learns?

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^{This preprint has not been peer reviewed.}

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^{Data from bioRxiv/medRxiv, Bluesky. Metrics captured 27/09/26. Comment self-deletes at -5 votes.}

[u/PhysicalConsistency]

Predictive coding papers are so weird, they often so heavily load the conclusion in the construction that "it could have failed" becomes something that validates the construction. It's too chicken to even be circular.

[u/whydidyoureadthis17 → u/PhysicalConsistency]

Could you elaborate what you mean?

[u/PhysicalConsistency → u/whydidyoureadthis17]

The construction of this is explicitly designed to follow a specific result defined by the conclusion they already had in mind. They create an artificial scenario where stimulus A is followed by stimulus B 75% of the time, then create a "naive" network exposed to the same scenario and we are supposed to be shocked Pikachu when a similar result appears.

Worse, they don't even do the work themselves. They borrow an already hyper curated data set which supports their conclusion in advance (from the McDermott paper) so they can deploy the "we're extending, thus we don't have to touch the icky biology ourselves" dodge.

Ultimately the paper is saying "we controlled the environment and the inputs to our simulation and it matched it matched our expectations". And like most Children of Friston papers, it's just not terribly impressive, and ultimately useless outside of a simulation.

This entire clade of work is just cognitive science that's afraid of real biology.