Description:
We present ways of defining neuromanifolds models of stochastic matrices that are compatible with the maximization of an objective function (reward in reinforcement learning, predictive information in robotics, information flow in neural networks). Our approach is based on information geometry and aims at the reduction of model parameters with the hope to improve gradient learning processes. We discuss advantages and shortcomings of this approach.