The capability of layered networks is studied, and important roles played by feedback connections will be studied. It is also studied how local and distributed representations in neural fields are mutually related and how they are integrated through dynamic interactions. Invariancy of recognition as well as mutual interactions of memory and cognition systems are also an important subject of research. Learning is one of the most important aspects of the brain that makes it so flexible. A learning system self-organizes under the guidance of adequately represented learning signals such that each subsystem plays a proper role.
Stochastic fluctuations play an important role in learning algorithms.
Mathematical foundations of learning will be established by means of information geometry. A model of IT areas in the cerebrum will be built based on theoretical considerations. In order to elucidate its capabilities as well as limitations to be overcome, we study the capacity of the model, dynamics of recalling processes, a role of chaos, effect of sparse representation, and functions of nonmonotone dynamics. A plausible model of the hippocampus and cerebrum system will be constructed. A neural model of the visual system will be constructed on these bases.