Deep learning for parameter recovery from a neural mass model of perceptual decision-making

Sicurella, Emanuele and Zhang, Jiaxiang ORCID: https://orcid.org/0000-0002-4758-0394 2022. Deep learning for parameter recovery from a neural mass model of perceptual decision-making. Presented at: Conference on Cognitive Computational Neuroscience, San Francisco, 25-28 August 2022. 2022 Conference on Cognitive Computational Neuroscience Proceedings. CCN, 10.32470/CCN.2022.1095-0

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Abstract

In neuroscience, parameter recovery refers to the problem of finding the best parameters of a model for fitting the experimental data. The developing of more biologically plausible computational models of cognition has offered a significant improvement in the predictive power at the cost of a higher complexity posing increasing challenges on parameter recovery. Here, we present a deep learning approach to recover parameters of a two-variables neural mass model simulating evidence accumulation during perceptual decision-making. We show that our algorithm is able to recovery well specific set of parameters but might fail when trying to predict combinations of parameters with a high degree of interaction, i.e. parameters that have inherently similar effects on the model’s output. Thus, our study suggests that deep learning for parameter recovery should go together with a carefully designed experiment to study the effects of different parameters that are not richly interacting.

Item Type:	Conference or Workshop Item (Poster)
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Psychology
Publisher:	CCN
Date of First Compliant Deposit:	27 September 2022
Date of Acceptance:	August 2022
Last Modified:	13 Jun 2024 15:12
URI:	https://orca.cardiff.ac.uk/id/eprint/152908

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