Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains highly debated. In particular, while forward activations of pretrained models reliably map onto the cortical hierarchy of visual processing, it is unknown whether backpropagated gradients exhibit a similar correspondence. Here, we address this question using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings of human brain responses to natural images. For this, we extend standard encoding analyses of forward activations to map backpropagated gradients onto neural data. Focusing on a recent self-supervised...
| Authors | Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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