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Optimising a computational model of human auditory cortex with an evolutionary algorithm

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Optimising a computational model of human auditory cortex with an evolutionary algorithm. / Tomana, Ewelina; Härtwich, Nina; Rozmarynowski, Adam et al.
In: Hearing Research, Vol. 439, 108879, 30.11.2023.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Tomana, E, Härtwich, N, Rozmarynowski, A, König, R, May, PJC & Sielużycki, C 2023, 'Optimising a computational model of human auditory cortex with an evolutionary algorithm', Hearing Research, vol. 439, 108879. https://doi.org/10.1016/j.heares.2023.108879

APA

Tomana, E., Härtwich, N., Rozmarynowski, A., König, R., May, P. J. C., & Sielużycki, C. (2023). Optimising a computational model of human auditory cortex with an evolutionary algorithm. Hearing Research, 439, Article 108879. https://doi.org/10.1016/j.heares.2023.108879

Vancouver

Tomana E, Härtwich N, Rozmarynowski A, König R, May PJC, Sielużycki C. Optimising a computational model of human auditory cortex with an evolutionary algorithm. Hearing Research. 2023 Nov 30;439:108879. Epub 2023 Aug 29. doi: 10.1016/j.heares.2023.108879

Author

Tomana, Ewelina ; Härtwich, Nina ; Rozmarynowski, Adam et al. / Optimising a computational model of human auditory cortex with an evolutionary algorithm. In: Hearing Research. 2023 ; Vol. 439.

Bibtex

@article{42c20c44627f4706a186d829f7355354,
title = "Optimising a computational model of human auditory cortex with an evolutionary algorithm",
abstract = "We demonstrate how the structure of auditory cortex can be investigated by combining computational modelling with advanced optimisation methods. We optimise a well-established auditory cortex model by means of an evolutionary algorithm. The model describes auditory cortex in terms of multiple core, belt, and parabelt fields. The optimisation process finds the optimum connections between individual fields of auditory cortex so that the model is able to reproduce experimental magnetoencephalographic (MEG) data. In the current study, this data comprised the auditory event-related fields (ERFs) recorded from a human subject in an MEG experiment where the stimulus-onset interval between consecutive tones was varied. The quality of the match between synthesised and experimental waveforms was 98%. The results suggest that neural activity caused by feedback connections plays a particularly important role in shaping ERF morphology. Further, ERFs reflect activity of the entire auditory cortex, and response adaptation due to stimulus repetition emerges from a complete reorganisation of AC dynamics rather than a reduction of activity in discrete sources. Our findings constitute the first stage in establishing a new non-invasive method for uncovering the organisation of the human auditory cortex.",
keywords = "Auditory cortex, Computational modelling, Event-related field, Evolutionary algorithms, MEG, Optimisation",
author = "Ewelina Tomana and Nina H{\"a}rtwich and Adam Rozmarynowski and Reinhard K{\"o}nig and May, {Patrick J.C.} and Cezary Sielu{\.z}ycki",
year = "2023",
month = nov,
day = "30",
doi = "10.1016/j.heares.2023.108879",
language = "English",
volume = "439",
journal = "Hearing Research",
issn = "0378-5955",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Optimising a computational model of human auditory cortex with an evolutionary algorithm

AU - Tomana, Ewelina

AU - Härtwich, Nina

AU - Rozmarynowski, Adam

AU - König, Reinhard

AU - May, Patrick J.C.

AU - Sielużycki, Cezary

PY - 2023/11/30

Y1 - 2023/11/30

N2 - We demonstrate how the structure of auditory cortex can be investigated by combining computational modelling with advanced optimisation methods. We optimise a well-established auditory cortex model by means of an evolutionary algorithm. The model describes auditory cortex in terms of multiple core, belt, and parabelt fields. The optimisation process finds the optimum connections between individual fields of auditory cortex so that the model is able to reproduce experimental magnetoencephalographic (MEG) data. In the current study, this data comprised the auditory event-related fields (ERFs) recorded from a human subject in an MEG experiment where the stimulus-onset interval between consecutive tones was varied. The quality of the match between synthesised and experimental waveforms was 98%. The results suggest that neural activity caused by feedback connections plays a particularly important role in shaping ERF morphology. Further, ERFs reflect activity of the entire auditory cortex, and response adaptation due to stimulus repetition emerges from a complete reorganisation of AC dynamics rather than a reduction of activity in discrete sources. Our findings constitute the first stage in establishing a new non-invasive method for uncovering the organisation of the human auditory cortex.

AB - We demonstrate how the structure of auditory cortex can be investigated by combining computational modelling with advanced optimisation methods. We optimise a well-established auditory cortex model by means of an evolutionary algorithm. The model describes auditory cortex in terms of multiple core, belt, and parabelt fields. The optimisation process finds the optimum connections between individual fields of auditory cortex so that the model is able to reproduce experimental magnetoencephalographic (MEG) data. In the current study, this data comprised the auditory event-related fields (ERFs) recorded from a human subject in an MEG experiment where the stimulus-onset interval between consecutive tones was varied. The quality of the match between synthesised and experimental waveforms was 98%. The results suggest that neural activity caused by feedback connections plays a particularly important role in shaping ERF morphology. Further, ERFs reflect activity of the entire auditory cortex, and response adaptation due to stimulus repetition emerges from a complete reorganisation of AC dynamics rather than a reduction of activity in discrete sources. Our findings constitute the first stage in establishing a new non-invasive method for uncovering the organisation of the human auditory cortex.

KW - Auditory cortex

KW - Computational modelling

KW - Event-related field

KW - Evolutionary algorithms

KW - MEG

KW - Optimisation

U2 - 10.1016/j.heares.2023.108879

DO - 10.1016/j.heares.2023.108879

M3 - Journal article

VL - 439

JO - Hearing Research

JF - Hearing Research

SN - 0378-5955

M1 - 108879

ER -