arXiv Machine Learning

Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology

arXiv:2605. 22401v2 Announce Type: replace Abstract: CORRECTION (August 2026): an evaluation-mode defect in the shared feature-extraction pipeline affected the predictive-coding and STDP conditions.

arXiv Machine Learning

2d ago

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

safety

arXiv Machine Learning

2d ago

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 4

Predicted Cortex Is Not a Domain-General Prior: A Matched-Control Audit of Brain-Encoding Features for Video Memorability

arXiv:2607. 16292v4 Announce Type: replace-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 Machine Learning

5d ago

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

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.

By Nils Leutenegger

llms safety

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 Machine Learning

Aug 4

What EEG Foundation Models Encode: Dataset Identity and a Negative-Control Suite for Clinical Benchmarks

arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.

By Marzieh Zare

rag fine-tuning benchmarks

arXiv Machine Learning

Jun 8

The Identity Trap in EEG Foundation Models: A Diagnostic Audit

arXiv:2606. 06647v1 Announce Type: new Abstract: Objective.

By Jun-You Lin, Ying Choon Wu, Tzyy-Ping Jung

fine-tuning benchmarks

arXiv AI

2d ago

A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing

arXiv:2607. 24519v3 Announce Type: replace-cross Abstract: EEG foundation-model gains may depend on cohort, montage, or probe design.

By Marzieh Zare

fine-tuning benchmarks

arXiv AI

Jul 29

Foundation Models for EEG Are Blind to Long-Range Temporal Correlations: A Spectral-Temporal Dissociation Behind Their Cross-Population Fragility

arXiv:2607. 24834v1 Announce Type: cross Abstract: Objective.

By Marzieh Zare

rag

arXiv AI

Jul 28

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

arXiv:2607. 24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear.

By Marzieh Zare

rag fine-tuning benchmarks

Hugging Face Trending Papers

Jul 27

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits.

rag fine-tuning benchmarks

arXiv Machine Learning

Aug 4

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

arXiv:2607. 24834v2 Announce Type: replace-cross Abstract: Objective.

By Marzieh Zare

rag

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