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: | Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > 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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