Deep Learning Approach for Life Emerged from High-dimensional Data Recognition

Abstract

Animal receives high-dimensional and complex raw sensory information. Deep learning can recognize such complex sensory information. We studied a deep learning model called hierarchical recurrent neural network (HRNN) that develops spatial recognition through visuomotor integration learning. In a simulation experiment, the HRNN developed the cognitive map, which is an objective map-like internal model, through only subjective visuomotor experiences. Furthermore, the HRNN also developed spatial recognition through visuomotor sequences by a human subject. These results imply that deep learning model can be used to study real animals’ cognition.