Deep neural networks (DNNs), which have been developed with reference to the network structures and the operational algorithms of the brain, have achieved notable success in a broad range of fields, including computer vision, in which they have produced results comparable to, and in some cases superior to, human experts. In recent years, DNNs have also been expected to be useful as a tool for studies of the brain. Recently a research team led by associate professor Eiji Watanabe of the National Institute for Basic Biology successfully reproduced illusory motion by DNNs trained for prediction. The DNNs are based on predictive coding theory (Figure 1), which assumes that the internal models of the brain predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. If the theory substantially reproduces the visual information processing of the brain, then the DNNs can be expected to represent the human vis...
