An encoding framework with brain inner state for natural image identification
This work addresses the challenge of improving neural encoding models in cognitive neuroscience by integrating inner state information, offering a flexible and robust approach for brain decoding tasks.
The authors tackled the problem of predicting brain activity from visual stimuli by proposing a novel encoding framework that incorporates both external stimuli and inner brain states, achieving significantly better performance in natural image identification from fMRI responses compared to forward-only models.
Neural encoding and decoding, which aim to characterize the relationship between stimuli and brain activities, have emerged as an important area in cognitive neuroscience. Traditional encoding models, which focus on feature extraction and mapping, consider the brain as an input-output mapper without inner states. In this work, inspired by the fact that human brain acts like a state machine, we proposed a novel encoding framework that combines information from both the external world and the inner state to predict brain activity. The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain. The forward model can be any traditional encoding model, making the framework flexible. The inner state model is a linear model to utilize information in the prediction residuals of the forward model. The proposed encoding framework can achieve much better performance on natural image identification from fMRI response than forwardonly models. The identification accuracy will decrease slightly with the dataset size increasing, but remain relatively stable with different identification methods. The results confirm that the new encoding framework is effective and robust when used for brain decoding.