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pro vyhledávání: '"King, Jean"'
Autor:
Thual, Alexis, Benchetrit, Yohann, Geilert, Felix, Rapin, Jérémy, Makarov, Iurii, Banville, Hubert, King, Jean-Rémi
Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI). However, the large inter-subject variability in brain characteristics has limited most studies to train models on one subject
Externí odkaz:
http://arxiv.org/abs/2312.06467
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fide
Externí odkaz:
http://arxiv.org/abs/2310.19812
During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master increasingly complex syntactic structures. However, the comput
Externí odkaz:
http://arxiv.org/abs/2306.03586
Decoding speech from brain activity is a long-awaited goal in both healthcare and neuroscience. Invasive devices have recently led to major milestones in that regard: deep learning algorithms trained on intracranial recordings now start to decode ele
Externí odkaz:
http://arxiv.org/abs/2208.12266
Autor:
Gwilliams, Laura, Flick, Graham, Marantz, Alec, Pylkkanen, Liina, Poeppel, David, King, Jean-Remi
The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fict
Externí odkaz:
http://arxiv.org/abs/2208.11488
Autor:
Millet, Juliette, Caucheteux, Charlotte, Orhan, Pierre, Boubenec, Yves, Gramfort, Alexandre, Dunbar, Ewan, Pallier, Christophe, King, Jean-Remi
Publikováno v:
Neural Information Processing Systems (NeurIPS), 2022
Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data,
Externí odkaz:
http://arxiv.org/abs/2206.01685
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. However, i
Externí odkaz:
http://arxiv.org/abs/2202.07290
Deep learning has recently made remarkable progress in natural language processing. Yet, the resulting algorithms remain far from competing with the language abilities of the human brain. Predictive coding theory offers a potential explanation to thi
Externí odkaz:
http://arxiv.org/abs/2111.14232
Publikováno v:
Findings of the Association for Computational Linguistics (EMNLP 2021)
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). Although successful, this `mode
Externí odkaz:
http://arxiv.org/abs/2110.06078