Modeling the dynamics of human brain activity with recurrent neural networks

TL;DR

This paper demonstrates that recurrent neural networks can effectively model the dynamic responses of the human brain to sensory stimuli, outperforming traditional response models by capturing long-term dependencies in brain activity.

Contribution

It introduces the use of recurrent neural networks as response models for brain activity prediction, highlighting their ability to capture long-term dependencies in neural responses.

Findings

Recurrent neural networks outperform traditional response models.

They accurately estimate long-term dependencies in brain activity.

The approach advances understanding of brain response dynamics.

Abstract

Encoding models are used for predicting brain activity in response to sensory stimuli with the objective of elucidating how sensory information is represented in the brain. Encoding models typically comprise a nonlinear transformation of stimuli to features (feature model) and a linear transformation of features to responses (response model). While there has been extensive work on developing better feature models, the work on developing better response models has been rather limited. Here, we investigate the extent to which recurrent neural network models can use their internal memories for nonlinear processing of arbitrary feature sequences to predict feature-evoked response sequences as measured by functional magnetic resonance imaging. We show that the proposed recurrent neural network models can significantly outperform established response models by accurately estimating long-term dependencies that drive hemodynamic responses. The results open a new window into modeling the dynamics of brain activity in response to sensory stimuli.