4 accepted papers
Inspired by cognitive neuroscience studies, we introduce a novel “decoding probing” method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the brain and its representations as “neural a…
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given their distinct training modalities, it remains unclear whether they correlate with the same neural aspects. We directly add…
Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural recordings into speech using complex deep neural network (DNN) models trained on extensive neural recording data, which…
Deposition of β-amyloid is a crucial biomarker to evaluate subjects with early-onset dementia, often evaluated through Aβ-PET imaging. Aβ-PET is expensive and radiation-heavy; thus, it’s advisable to avoid it unless medically necessary. Therefore there is a compelling need to classify Aβ and detect…