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Neural encoding and decoding, which aim to characterize the relationshipbetween stimuli and brain activities, have emerged as an important area incognitive neuroscience. Traditional encoding models, which focus on featureextraction and mapping, consider the brain as an input-output mapper withoutinner states. In this work, inspired by the fact that human brain acts like astate machine, we proposed a novel encoding framework that combines informationfrom both the external world and the inner state to predict brain activity. Theframework comprises two parts: forward encoding model that deals with visualstimuli and inner state model that captures influence from intrinsicconnections in the brain. The forward model can be any traditional encodingmodel, making the framework flexible. The inner state model is a linear modelto utilize information in the prediction residuals of the forward model. Theproposed encoding framework can achieve much better performance on naturalimage identification from fMRI response than forwardonly models. Theidentification accuracy will decrease slightly with the dataset sizeincreasing, but remain relatively stable with different identification methods.The results confirm that the new encoding framework is effective and robustwhen used for brain decoding.

An encoding framework with brain inner state for natural image identification