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:2607. 01400v1 Announce Type: cross Abstract: Deep multimodal brain-encoding models now predict fMRI responses to naturalistic video with high accuracy.
By Barada Sahu, Shivesh Pandey
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
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
arXiv:2609.12090v1 Announce Type: new
Abstract: Video models increasingly use memory to preserve information over long sequences, with the assumption that gains come from retrieving and using the cor...
By Aditi Tiwari, Akshit Bhalla, Darshan Prasad, Heng Ji
arXiv:2606. 31495v1 Announce Type: new Abstract: We study a single idea across two settings: that a prediction-error signal, computed by a small predictor over the latent space of a frozen encoder, can serve both as a gate on plasticity and as a substrate for metacognition.
By Louis Mouchon