Conference on Cognitive Computational Neuroscience

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.