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MiS Preprint

Selection Criteria for Neuromanifolds of Stochastic Dynamics

Nihat Ay, Guido Montúfar and Johannes Rauh


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.

Apr 19, 2011
Apr 19, 2011
MSC Codes:
62B10, 82C32
learning, neural nets, information geometry

Related publications

2013 Repository Open Access
Nihat Ay, Guido Montúfar and Johannes Rauh

Selection criteria for neuromanifolds of stochastic dynamics

In: Advances in cognitive neurodynamics III : proceedings of the 3rd International Conference on Cognitive Neurodynamics 2011 ; [June 9-13, 2011, Hilton Niseko Village, Hokkaido, Japan] / Yoko Yamaguchi (ed.)
Dordrecht : Springer, 2013. - pp. 147-154
(Advances in cognitive neurodynamics)