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Backpropagation degrades neural network brain alignment within one epoch

By PulseAugur Editorial · [2 sources] ·

A new research paper reveals that standard supervised training methods, particularly backpropagation, can rapidly degrade the alignment of artificial neural networks with the early visual cortex of the human brain. This degradation occurs within a single training epoch, suggesting that untrained networks may capture low-level visual statistics more effectively due to inherent inductive biases. Alternative learning rules like predictive coding and spike-timing-dependent plasticity show less severe degradation, preserving more brain-like structure in early visual representations. AI

IMPACT Suggests current training methods may hinder AI models from achieving optimal representational similarity with biological vision systems.

RANK_REASON Academic paper detailing novel findings on AI model training and biological brain alignment.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Backpropagation degrades neural network brain alignment within one epoch

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Academic paper detailing novel findings on AI model training and biological brain alignment.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Nils Leutenegger ·

    Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules

    arXiv:2605.30556v1 Announce Type: new Abstract: Random, untrained neural networks consistently match or exceed trained networks in representational similarity to early visual cortex. This puzzling finding challenges the assumption that learning improves brain alignment. We invest…

  2. r/MachineLearning TIER_1 English(EN) · /u/ConfusionSpiritual19 ·

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

    <!-- SC_OFF --><div class="md"><p>Third in a series of papers tracking learning rules vs. human fMRI (THINGS dataset, V1–IT, N=3 subjects).</p> <p>Previous finding: untrained CNNs match backprop at V1. This paper asks: when does training break that, and does the learning rule mat…

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