arXiv Machine Learning August 14, 2026 By Nils Leutenegger

Evaluation Resolution Confounds Learning-Rule Comparisons in Model-Brain RSA of Early Visual Cortex

Read the original on arXiv Machine Learning →

arXiv:2608. 12408v1 Announce Type: cross Abstract: Representational similarity analysis (RSA) is increasingly used to ask which learning rules give convolutional networks brain-like representations.

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arXiv Machine Learning

Aug 17

Untrained CNNs Match Backpropagation at V1: A Systematic RSA Comparison of Four Learning Rules Against Human fMRI

arXiv:2604. 16875v3 Announce Type: replace Abstract: CORRECTION (August 2026): an evaluation-mode defect affected the predictive-coding and STDP conditions of this study; those results should not be used pending re-computation.

By Nils Leutenegger

safety

arXiv Machine Learning

Aug 17

Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules

arXiv:2605. 30556v2 Announce Type: replace Abstract: CORRECTION (August 2026): the central finding of this paper is not supported.

By Nils Leutenegger

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arXiv Computer Vision

Sep 23

Do Vision Model See Like the Brain? A Comparison Across EEG Encoding Model

arXiv:2609.26512v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) and vision transformers are both used to model the human visual system, but whether the two architectures diverge...

By Shashank Baghel, Kshitij Dwivedi, Dinesh Singh, Sanjeev Nara

llms

arXiv AI

2d ago

Useful to Whom? Sample Value Is Defined Only Relative to the Learner

The paper investigates how the usefulness of training samples, as determined by coreset selection, depends on the learner rather than just the data. Experiments on ImageNet-100 and ImageNet-1k show that changing model width, input grid, stride, and architecture (e.g., ResNet vs. ViT) shifts the crossover point where different selection criteria (easy-first vs. geometric coverage) become optimal. These findings demonstrate that the relative value of a fixed subset of samples varies with the target learner’s capacity and structure, and that selection strategies must be tuned to the specific model they will train.

By Yangze Liu, Xiao-Long Yin, Zhongyi Han

arXiv AI

Jul 21

It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability

arXiv:2607. 16292v1 Announce Type: cross Abstract: Brain-encoding foundation models predict fMRI responses to video, audio, and text well enough to win the Algonauts 2025 challenge.

By Carson Rodrigues

arXiv Computer Vision

2d ago

Two Routes to the Middle: Placement Search and Brain Readouts Converge on Where Continual Learners Should Specialize

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By Yuan Huang, Zihan Chen, Runbin Zhang, Hongwei Ding, Changzeng Fu, Shiqi Zhao

llms computer-vision