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  1. Queen Mary University of London
  2. Research
  3. Publications

Publications: MR Tom Kaplan

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Naderi H, Kaplan T, van Duijvenboden S, Pujadas ER, Aung N, Anwar A Chahal C, Lekadir K, Chamling B et al. ( 2026 ) . Deep learning to predict left ventricular hypertrophy from the electrocardiogram . EP Europace
Damsma A, Cannon J, Fink LK, Doelling KB, Grahn JA, Honing H, Kaplan T, Large EW et al. ( 2025 ) . Computational modeling of rhythmic expectations: Perspectives, pitfalls, and prospects . PLOS Computational Biology vol. 21 , ( 12 )
( 2025 ) . Abstract representations underlie rhythm perception and production: Evidence from a probabilistic model of temporal structure . Cognition vol. 268 ,
Naderi H, Kaplan T, Duijvenboden SV, Pujadas ER, Aung N, A Chahal CA, Lekadir K, Chamling B et al. ( 2025 ) . 6-015 Deep learning to predict left ventricular hypertrophy from the electrocardiogram . Conference: Imaginga186.2 - a1a188 .
Cannon J, Kaplan T ( 2024 ) . Inferred representations behave like oscillators in dynamic Bayesian models of beat perception . Journal of Mathematical Psychology vol. 122 ,
Clemente A, Kaplan TM, Pearce MT ( 2024 ) . Perceptual representations mediate effects of stimulus properties on liking for music . Annals of the New York Academy of Sciences vol. 1533 , ( 1 ) 169 - 180 .
Kaplan T, Jamone L, Pearce M ( 2023 ) . Probabilistic modelling of microtiming perception . Cognition vol. 239 ,
Kaplan T, Cannon J, Jamone L, Pearce M ( 2022 ) . Modeling enculturated bias in entrainment to rhythmic patterns . PLOS Computational Biology vol. 18 , ( 9 )
Kaplan T, Chew E . Detecting Low Frequency Oscillations in Cardiovascular Signals Using Gradient Frequency Neural Networks . 2016 Computing in Cardiology Conference (CinC) . Conference: 2019 Computing in Cardiology Conference (CinC) vol. 45 ,
Kaplan T, Chew E ( 2019 ) . Detecting Low Frequency Oscillations in Cardiovascular Signals Using Gradient Frequency Neural Networks . Computing in Cardiology . vol. 2019-September ,
Bouwer FL, Damsma A, Kaplan T, Sarvestani MG, Pearce MT . Abstract representations underlie rhythm perception and production: Evidence from a probabilistic model of temporal structure .
Damsma A, Cannon J, Fink L, Doelling KB, Grahn JA, Honing H, Kaplan T, Large E et al. . Computational Modeling of Rhythmic Expectations: Perspectives, Pitfalls, and Prospects .
Kaplan T, Jamone L, Pearce MT . Probabilistic modelling of microtiming perception .
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