Deep learning as a computational model in functional neuroimaging

Abstract

Functional neuroimaging (FNI) plays an essential role in cognitive science investigating information processes in cognitive mechanisms. Computational models that explain the behavior and its underlying information processing have become indispensable for the functional mapping of cognition in the FNI field. However, it is challenging to use computational models consisting of simple equations and several parameters to reveal the distributed representation of information processing in the brain. Machine learning has analyzed the activation pattern for information processing in the brain. Even before the deep learning revolution, machine learning was used to predict brain activity patterns from stimuli (i.e., encoding) and to discriminate or reconstruct the stimuli and behavior from brain activity (i.e., decoding). Convolutional neural network (CNN), one of the deep neural networks (DNNs) mimicking the visual nervous system for object recognition, was a pioneering example of the potential of deep learning as a computational model of the brain. The activity of the middle layers of CNN can reflect distributed processes for object recognition in the ventral visual pathway. To use DNNs as computational models of FNI for more broad perceptions and cognitions, the activity of the middle layer of DNN should correspond to the activation of a brain region. This article briefly reviews the computational models of FNI and deep learning included in FNI machine learning and discusses the DNN as a computational model in FNI. We suggest that deep learning can serve as a computational model in FNI, connecting the activation pattern in the brain and hierarchical/distributed cognitive processes.