Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Barry Devereux"'
Autor:
Cai Wingfield, Chao Zhang, Barry Devereux, Elisabeth Fonteneau, Andrew Thwaites, Xunying Liu, Phil Woodland, William Marslen-Wilson, Li Su
Publikováno v:
Frontiers in Computational Neuroscience, Vol 16 (2022)
IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance,
Externí odkaz:
https://doaj.org/article/9ee534075fed4546b6a09d781297bd19
Publikováno v:
PLoS ONE, Vol 14, Iss 9, p e0214342 (2019)
Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which
Externí odkaz:
https://doaj.org/article/03b1f6956e854310ab6de97e8e423f92
Autor:
Barry Devereux, Paul Hoffman, Loris Naspi, Leonidas A. A. Doumas, Alexa M. Morcom, Tobias Thejll-Madsen
Publikováno v:
Naspi, L, Hoffman, P, Devereux, B, Thejll-Madsen, T, Doumas, L A A & Morcom, A 2021, ' Multiple dimensions of semantic and perceptual similarity contribute to mnemonic discrimination for pictures ', Journal of Experimental Psychology: Learning, Memory, and Cognition . https://doi.org/10.1037/xlm0001032
People often misrecognize objects that are similar to those they have previously encountered. These mnemonic discrimination errors are attributed to shared memory representations (gist) typically characterized in terms of meaning. In two experiments,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6766e9ad493aef88e5394b7b92a18ed
https://doi.org/10.1037/xlm0001032
https://doi.org/10.1037/xlm0001032
Publikováno v:
JAMIA Open
Phenotypes are the result of the complex interplay between environmental and genetic factors. To better understand the interactions between chemical compounds and human phenotypes, and further exposome research we have developed “phexpo,” a tool
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::105ebb63996376fe43b5da44df03653a
https://doi.org/10.1093/jamiaopen/ooaa023
https://doi.org/10.1093/jamiaopen/ooaa023
To better understand the computational steps that the brain performs during reading, we used a convolutional neural network as a computational model of visual word recognition, the first stage of reading. In contrast to traditional models of reading,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6bd74a62e4154ebf42ce6724986f5b6d
https://doi.org/10.1101/2022.02.08.479654
https://doi.org/10.1101/2022.02.08.479654
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
Understanding the interactions between novel drugs and target proteins is fundamentally important in disease research as discovering drug-protein interactions can be an exceptionally time-consuming and expensive process. Alternatively, this process c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a400d5e6ed55bdc4555fe4cdf564e405
https://pubmed.ncbi.nlm.nih.gov/34892183
https://pubmed.ncbi.nlm.nih.gov/34892183
Publikováno v:
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
Modern sequencing technology has produced a vast quantity of proteomic data, which has been key to the development of various deep learning models within the field. However, there are still challenges to overcome with regards to modelling the propert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fa1eae0a3b30f76f9949058fa51dd9d
https://doi.org/10.1109/embc46164.2021.9630387
https://doi.org/10.1109/embc46164.2021.9630387
Publikováno v:
Naspi, L, Hoffman, P, Devereux, B & Morcom, A M 2021, ' Perceptual and Semantic Representations at Encoding Contribute to True and False Recognition of Objects ', The Journal of neuroscience : the official journal of the Society for Neuroscience, vol. 41, no. 40, pp. 8375-8389 . https://doi.org/10.1523/JNEUROSCI.0677-21.2021
J Neurosci
Naspi, L, Hoffman, P, Devereux, B & Morcom, A 2021, ' Perceptual and semantic representations at encoding contribute to true and false recognition of objects ', Journal of Neuroscience, vol. 41, no. 40, JN-RM-0677-21, pp. 8375-8389 . https://doi.org/10.1523/JNEUROSCI.0677-21.2021
J Neurosci
Naspi, L, Hoffman, P, Devereux, B & Morcom, A 2021, ' Perceptual and semantic representations at encoding contribute to true and false recognition of objects ', Journal of Neuroscience, vol. 41, no. 40, JN-RM-0677-21, pp. 8375-8389 . https://doi.org/10.1523/JNEUROSCI.0677-21.2021
When encoding new episodic memories, visual and semantic processing are proposed to make distinct contributions to accurate memory and memory distortions. Here, we used functional magnetic resonance imaging (fMRI) and representational similarity anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10e4f75e9cce5a696afcd9cde19a3fd9
https://pure.qub.ac.uk/en/publications/6fb19a08-6dd1-42df-bbf5-f9e2fc58576f
https://pure.qub.ac.uk/en/publications/6fb19a08-6dd1-42df-bbf5-f9e2fc58576f
Publikováno v:
Derby, S, Miller, P & Devereux, B 2021, Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models . in E Chersoni, N Hollenstein, C Jacobs, Y Oseki, L Prevot & E Santus (eds), CMCL 2021-Workshop on Cognitive Modeling and Computational Linguistics, Proceedings . CMCL 2021-Workshop on Cognitive Modeling and Computational Linguistics, Proceedings, Association for Computational Linguistics (ACL), pp. 211-221, 11th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2021, Virtual, Online, 10/06/2021 . < https://aclanthology.org/2021.cmcl-1.25 >
CMLS
CMLS
Neural network language models have the ability to capture the contextualised meanings of words in a sentence by dynamically evolving a representation of the linguistic input in a manner evocative of human language comprehension. While researchers ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aca8dbc21d08a52d6520729e10b2b8d4
https://pure.qub.ac.uk/en/publications/representation-and-preactivation-of-lexicalsemantic-knowledge-in-neural-language-models(7b6d72af-c770-4022-9b6b-2efe88a526cd).html
https://pure.qub.ac.uk/en/publications/representation-and-preactivation-of-lexicalsemantic-knowledge-in-neural-language-models(7b6d72af-c770-4022-9b6b-2efe88a526cd).html
Publikováno v:
Lennox, M, Devereux, B & Robertson, N 2021, Deep Metric Learning for Proteomics . in M A Wani, F Luo, X Li, D Dou & F Bonchi (eds), Proceedings-19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 ., 9356221, Proceedings-19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 308-313, 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, Virtual, Miami, United States, 14/12/2020 . https://doi.org/10.1109/ICMLA51294.2020.00057
ICMLA
ICMLA
Deep learning has become an innovative tool for predicting the properties of a protein. However, obtaining an accurate predictive model using deep learning methods typically requires a large amount of labelled data, which is expensive and time-consum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b4c334fac0d593750ddea9ed7d0f088
https://pure.qub.ac.uk/en/publications/dac86f6c-b754-4226-8c30-4c47b99dc59c
https://pure.qub.ac.uk/en/publications/dac86f6c-b754-4226-8c30-4c47b99dc59c