Study finds evaluation resolution skews brain-similarity claims in neural networks

What happened

Researchers tested five learning rules (random initialization, backpropagation, feedback alignment, predictive coding, STDP) on a small CNN trained at 32 pixels, then evaluated the results across six resolutions from 32px to 224px using THINGS-fMRI stimuli. They found that the commonly reported claim—that untrained CNNs match or exceed backpropagation-trained models when compared to brain activity in V1 (early visual cortex)—is largely an artifact of the resolution at which the comparison is made.

Why it matters

Model-brain comparisons using recurrent self-attention (RSA) are a standard way neuroscientists and AI researchers validate whether neural networks capture brain-like computation. If the apparent advantage of untrained networks vanishes when evaluation resolution changes, it means prior conclusions about which learning rule best mirrors the brain may be unreliable—researchers need to be careful about the resolution they choose when making these claims.

What to watch

The preprint (arxiv.org/abs/2608.12408) and accompanying code (github.com/nilsleut/evaluation-resolution-rsa) are now available. The study demonstrates a non-monotonic trend in the gap between trained and untrained backpropagation models across resolutions, suggesting that future model-brain work should test across multiple evaluation resolutions rather than relying on a single standard.