Author

Elisabeth Fonteneau

MRC Cognition and Brain Sciences Unit

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:benchmarking· 2 Assay:electronic health records· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1

Frequent Co-Authors

Andrew ThwaitesiD· 2 shared Cai WingfieldiD· 2 shared Li SuiD· 2 shared Philip C. WoodlandiD· 2 shared William D. Marslen‐WilsoniD· 2 shared Xunying LiuiD· 2 shared Barry J. DevereuxiD· 1 shared Béla WeissiD· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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2 shared papersNeuroscience

Andrew ThwaitesORCID

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3 papers

Recent·Most cited

On the similarities of representations in artificial and brain neural networks for speech recognition

Cai Wingfield, Chao Zhang, Barry J. Devereux, Elisabeth Fonteneau, Andrew Thwaites, Xunying Liu, Philip C. Woodland, William D. Marslen‐Wilson, Li Su

This study compares speech recognition representations in artificial neural networks and human brains, finding similarities and using brain data to improve machine models.

Frontiers in Computational Neuroscience · Dec 21, 2022Peer-reviewedNeuroscience

Investigating brain mechanisms underlying natural reading by co-registering eye tracking with combined EEG and MEG

Béla Weiss, Felix R. Dreyer, Elisabeth Fonteneau, Maarten van Casteren, Olaf Hauk

The paper demonstrates the feasibility of co-registering eye tracking with combined EEG and MEG to study natural reading by analyzing brain responses to sentence-final words.

bioRxiv · Jul 9, 2021 · PreprintNeuroscience

Relating dynamic brain states to dynamic machine states: human and machine solutions to the speech recognition problem

Cai Wingfield, Li Su, Xunying Liu, Chao Zhang, Philip C. Woodland, Andrew Thwaites, Elisabeth Fonteneau, William D. Marslen‐Wilson

This paper compares dynamic brain states during speech comprehension with dynamic machine states in ASR systems, finding correspondence between neural patterns and phonetic models.

PLoS Computational Biology · Sep 25, 2017Peer-reviewedNeuroscience