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Disentangled EFM Fine-Tuning (DEFT) is proposed, a plug-and-play adapter for EFM fine-tuning that improves matched fine-tuning baselines and encourages branch disentanglement of pretrained tokens into task-oriented content and subject-consistent style.
FReD is introduced, which derives fMRI representations from a frozen Deep Compression AutoEncoder pre-trained exclusively on natural images and pairs them with a task specific readout, making frozen natural-image features as a useful baseline for assessing its added value on current fMRI benchmarks.
By pre-training artificial neural networks exclusively on large-scale synthetic data, this work demonstrates robust zero-shot generalization across diverse brain regions, experimental paradigms and species, enabling the accurate inference of single-unit activities and cell-type properties without exposure to real data.
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