Séminaire NEURO : Jun Tani

Titre du séminaire et orateur

Bridging the Gap between Probabilistic and Deterministic Models : A Proposal of a Variational Bayes Predictive Coding Recurrent Neural Network Model.

Jun Tani, Professor, Cognitive Neurobotics Research Unit, Okinawa Institute of Science and Technology (OIST).

Date et lieu

Mardi 22 mai 2018, 10h30

UCP Cergy-Pontoise, St Martin, salle 570


The current talk proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The talk concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.