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Found 12 papers, 1 papers with code
Brain-to-Text Decoding: A Non-invasive Approach via Typing
For this, we present Brain2Qwerty, a new deep learning architecture trained to decode sentences from either electro- (EEG) or magneto-encephalography (MEG), while participants typed briefly memorized sentences on a QWERTY keyboard.
From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production
This approach confirms the hierarchical predictions of linguistic theories: the neural activity preceding the production of each word is marked by the sequential rise and fall of context-, word-, syllable-, and letter-level representations.
Scaling laws for decoding images from brain activity
Overall, these findings delineate the path most suitable to scale the decoding of images from non-invasive brain recordings.
Decoding individual words from non-invasive brain recordings across 723 participants
To tackle this issue, we introduce a novel deep learning pipeline to decode individual words from non-invasive electro- (EEG) and magneto-encephalography (MEG) signals.
A polar coordinate system represents syntax in large language models
However, this syntactic code remains incomplete: the distance between the Structural Probe word embeddings can represent the existence but not the type and direction of syntactic relations.
Aligning brain functions boosts the decoding of visual semantics in novel subjects
Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI).
Brain decoding: toward real-time reconstruction of visual perception
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity.
Language acquisition: do children and language models follow similar learning stages?
To investigate this, we here compare the learning trajectories of deep language models to those of children.
Decoding speech perception from non-invasive brain recordings
Overall, this effective decoding of perceived speech from non-invasive recordings delineates a promising path to decode language from brain activity, without putting patients at risk for brain surgery.
Don't stop the training: continuously-updating self-supervised algorithms best account for auditory responses in the cortex
Over the last decade, numerous studies have shown that deep neural networks exhibit sensory representations similar to those of the mammalian brain, in that their activations linearly map onto cortical responses to the same sensory inputs.
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e. g. regular speech versus scrambled words, sentences, or paragraphs).
Can RNNs learn Recursive Nested Subject-Verb Agreements?
Finally, probing the internal states of the model during the processing of sentences with nested tree structures, we found a complex encoding of grammatical agreement information (e. g. grammatical number), in which all the information for multiple words nouns was carried by a single unit.
