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

Reddit r/MachineLearning Papers
brain-alignment fmri backpropagation predictive-coding stdp learning-rules rsa

Summary

This paper tracks how different learning rules (backprop, feedback alignment, predictive coding, STDP) affect the alignment of CNN representations with human fMRI across training. It finds that backprop destroys V1 alignment in one epoch, while local rules preserve it, suggesting a trade-off between building higher-level representations and retaining early visual features.

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](http://arxiv.org/abs/2605.30556) Companion: [arxiv.org/abs/2604.16875](http://arxiv.org/abs/2604.16875) Code: [github.com/nilsleut](http://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.
Original Article

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