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Found 7 papers, 1 papers with code
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.
