[2D2-OS-21a] [Organized Session] OS-21

[2D2-OS-21a-03] Deep Learning Approach for Life Emerged from High-dimensional Data Recognition

〇Wataru Noguchi¹, Hiroyuki Iizuka¹, Masahito Yamamoto¹ (1. Hokkaido University)

Keywords:Artificial Life, Deep Learning

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.