Properties of learning curves and generalization abilities are searched for on this basis. We also establish and elaborate the Riemannian natural gradient learning algorithm to be applicable to brain-style computation. It is also important to verify how such theoretical principles are realized in the actual brain systems. The memory models so far proposed are mostly static, but dynamical aspects are much more important. We construct dynamical models of memory by using non-monotonic structure caused by neural inhibition and search for the mechanisms and efficiencies of memory search by chaos dynamics.
Stochastic fluctuations play a fundamental role in memory systems.
We apply information geometry to stochastic neural networks. We further search for a realistic model of hippocampal and cortical systems from this point of view. We also construct a model of learning in motor systems. Independent component analysis applied to MEG data We establish and elaborate independent component analysis as a new statistical and information-theoretic method of brain data analysis. We will apply this to MEG data in cooperation with University of Tokyo and others.