predictive coding

We developed a new cognitive architecture based on predictive coding and free-energy minimization for learning and re-creating actively memory sequences robustly, even with intrinsic noise or external perturbation.

The neural architecture is called INFERNO for Iterative Free-Energy Optimization for Recurrent Neural Networks. By learning the cause-effect relationship in a neural network, the architecture regenerates the specific dynamics learned in its memory by active inference. It is used on vocal learning and handwriting to learn the sensorimotor primitives.

current Phd student : Louis Annabi

Article Source: Iterative free-energy optimization for recurrent neural networks (INFERNO)

Alexandre Pitti, Mathias Quoy, Sofiane Boucenna, Catherine Lavandier. Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization. PLOS Computational Biology 17(2): e1008566

Louis Annabi, Alexandre Pitti, Mathias Quoy. Autonomous learning and chaining of motor primitives using the Free Energy Principle. IJCNN 2020. preprint ⟨hal-02567225⟩

Alexandre Pitti, Mathias Quoy, Catherine Lavandier, Sofiane Boucenna. Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE). Neural Networks. 2020. 121, 242-258. preprint ⟨hal-02140049⟩

Pitti A, Gaussier P, Quoy M (2017) Iterative free-energy optimization for recurrent neural networks (INFERNO). PLOS ONE 12(3): e0173684. doi: 10.1371/journal.pone.0173684

Pitti A., Braud R., Mahé S., Quoy M. & Gaussier P., Neural Model for Learning-to-Learn of Novel Task Sets in the Motor Domain, Frontiers in Psycholology, August 2013, vol.4, no.771, doi: 10.3389/fpsyg.2013.00771.

Pitti A. & Kuniyoshi Y. (2011) Modeling the Cholinergic Innervation in the Infant Cortico-Hippocampal System and its Contribution to Early Memory Development and Attention. Proc. of the International Joint Conference on Neural Networks IJCNN 2011, pp.1409-1416.