Backpropagation destroys V1 brain alignment in one epoch, tracking RSA alignment to fMRI across training for BP, FA, predictive coding, and STDP [R]

Third in a series of papers tracking learning rules vs. human fMRI (THINGS dataset, V1–IT, N=3 subjects).

Previous finding: untrained CNNs match backprop at V1. This paper asks: when does training break that, and does the learning rule matter?

Setup: RSA alignment measured at 8 checkpoints (epochs 0, 1, 2, 5, 10, 20, 30, 40), 5 seeds per rule, same architecture throughout.

Main findings:

1. BP drops 90% of V1 alignment after one epoch (r: 0.102 → 0.011, p = 0.031, consistent across all 5 seeds). FA drops 49%. PC and STDP drop only 25–31% and stabilise.
2. By epoch 40: PC (r = 0.064) > STDP (0.059) >> BP (0.022) ≈ FA (0.019). Cohen's d > 5 for PC/STDP vs BP: extremely consistent across seeds.
3. Opposing trend at LOC: BP shows a small increase in object-selective cortex alignment (+0.011) while local rules show nothing. Suggests a fundamental trade-off: global error signals build higher representations but destroy early ones.
4. Degradation rate tracks error signal globality: exact gradients (BP) > random feedback (FA) > local prediction errors (PC, STDP).

Limitations worth noting:

• 5 seeds caps permutation test resolution at p ≈ 0.031
• Training on 32×32 CIFAR-10, evaluated on 224×224 THINGS, resolution/domain shift is a confound
• LOC increase not tested for significance, treated as suggestive

Paper: arxiv.org/abs/2605.30556

Companion: arxiv.org/abs/2604.16875

Code: github.com/nilsleut

Curious whether anyone has seen similar dynamics in larger architectures, the prediction would be that deeper models show the same pattern but more slowly.