Untrained CNNs Exceed Backpropagation in V1 Alignment at High Evaluation Resolution: A Systematic RSA Comparison of Four Learning Rules Against Human fMRI

Abstract

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. At V1, the untrained baseline exceeds BP ($\rho = 0.053$ vs. $0.022$; $\Delta\rho = +0.031$, 95% CI $[0.019, 0.043]$), and the same holds at V2. This advantage depends on the evaluation resolution: in the 5-seed model set of the resolution study (gap $+0.030$ at 224 px), it vanishes when the models are evaluated at their 32 px training resolution ($\Delta\rho = -0.001$, 95% CI $[-0.011, 0.009]$; arXiv:2608.12408). At LOC, BP, PC and STDP exceed the untrained baseline, FA is borderline, and the trained rules do not differ from each other. At IT, no two conditions can be distinguished at the low reliability of the data (leave-one-subject-out lower bound 0.021). FA has the lowest alignment at V1. At V1, an untrained network thus matches BP at the training resolution and exceeds it at 224 px; learning-rule differences are resolvable only at an intermediate area.