Deep Learning for Parameter Recovery from a Neural Mass Model of Perceptual Decision-Making
Emanuele Sicurella, Jiaxiang Zhang, Cardiff University, United Kingdom
Posters 2 Poster
Pacific Ballroom H-O
Fri, 26 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
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.