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This paper introduces geometric stability as a measure of how reliably pairwise stimulus distances reproduce across trials, demonstrating its behavioral relevance and circuit dependence across brain regions, with an attractor network model explaining its emergence.
This paper tracks how supervised training with different learning rules (backpropagation, feedback alignment, predictive coding, STDP) degrades alignment between neural network representations and early visual cortex fMRI data, finding that untrained networks often match or exceed trained ones in V1 alignment.
This paper investigates the Platonic Representation Hypothesis by examining 16 language models across 8 families on 800 reasoning problems. It finds that while models converge in internal representations, they diverge in reasoning processes, especially post-decision, and shared representations have minimal causal influence on predictions.