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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 proposes DSFM, a novel generative framework that uses wavelet decomposition and spectral flow matching to synthesize realistic fMRI time series for brain disorder identification, addressing data scarcity and non-stationarity challenges.
Investigates neural integral-operator-based models for fMRI encoding and decoding tasks, focusing on the role of nonlocal spatiotemporal context and showing that larger temporal windows improve performance across datasets.