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Reconstructing from these common spaces back to each subject's original neural space, MED-VAE preserves equal stimulus-driven signal in its cro","title":"Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE","url":"https://arxiv.org/abs/2606.15989","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.15989v1 Announce Type: cross \nAbstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders. However, traditional alignment methods require shared stimuli across subjects, a constraint that limits applicability to naturalistic paradigms with limited or non-overlapping data. We introduce a Multi-Encoder-Decoder Variational Autoencoder (MED-VAE) that achieves cross-subject alignment without shared stimuli by anchoring representations to a common scaffold provided by a pretrained ANN. 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