Neural correlates of face perception modeled with a convolutional recurrent neural network.

Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We aimed to develop a computational model of visual ERP generation to study this phenomenon which consisted of a three-dimensional convolutional neural network (CNN) connected to a recurrent neural network (RNN).The CNN provided image representation learning, complimenting sequence learning of the RNN for modeling visually-evoked potentials.