Untrained CNNs Exceed Backpropagation in V1 Alignment at High Evaluation Resolution: A Systematic RSA Comparison of Four Learning Rules Against Human fMRI
Revised version (v4): noise ceiling replaced, analyses restricted to stimulus pairs presented in different fMRI runs, repaired evaluation of all five conditions. We compare four learning rules (backpropagation (BP), feedback alignment (FA), predictive coding (PC), and spike-timing-dependent plasticity (STDP)) in identical convolutional networks against human fMRI from THINGS-fMRI (720 stimuli, 3 subjects) using representational similarity analysis (RSA), together with an untrained random-weights baseline that isolates the contribution of architecture. Models are trained on 32 px CIFAR-10 and evaluated at 224 px; results are averaged across 5 seeds, with uncertainty from resampling stimuli...
| Authors | Nils Leutenegger |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 3 October 2026 |
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