Information Geometry of Neural Networks | New Bayesian Duality Theory |

| Information geometry is a method of analyzing the geometrical structure of a family of information systems. A family of neural networks forms a neuromanifold. It is important to study its geometrical structures for elucidating its capabilities of information processing. The present paper proposes a new mathematical theory of dynamic interactions of a lower and a higher neural systems by feedback and feedforward connections. Here, a new duality structure is introduced in the Bayesian framework from the point of view of information geometry.