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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 whether fMRI representations from different subjects' visual cortices can be aligned using unsupervised geometric methods, finding evidence for approximately isometric structure across individuals, extending the Platonic Representation Hypothesis to human brains.