Principal investigator (Inria):
Prof. Frederic Alexandre, Mnemosyne Team
Director of Research Inria
Principal investigator (partner):
Professor. Shan Yu
Brainnetome Center and National Laboratory of Pattern Recognition
Artificial Intelligence (AI) has been built on the opposition between symbolic problem solving that should be addressed by explicit models of planning, and numerical learning that should be obtained by neural networks (Dreyfus & Dreyfus, 1991; Sun & Alexandre, 2013). But it is clear that in ecological conditions, our cognition has to mix both capabilities and this is nicely carried out by our brains. Our behavior is sometimes described as a simple dichotomy between Goal-Directed (explicit deliberation and knowledge manipulation for planning) and habitual (automatic Stimulus-Response association) approaches. Recent results rather report more general strategies, including the hybrid combinations of both (Dolan and Dayan, 2013). Importantly, they highlight key mechanisms, corresponding to detect explicitly contexts in which the strategy should be modified and to adapt simple Stimulus-Response associations to these contexts.
The research expertise of the Inria-CAS joint team will synergize to provide a unique leverage to address this important issue. On the Chinese side, connectionist models like deep neural networks are adapted to avoid so-called catastrophic forgetting and to facilitate context-based information processing (Zeng et al., 2019). This is done by a clever mechanism of weight modification to protect previously learned associations, and by a module learning to detect and reuse corresponding contexts to flexibly alter the Stimulus- Response association learned by the neural networks. On the French side, models in computational neuroscience explore the capacity of neuronal structures like the hippocampus to categorize contexts (Kassab & Alexandre, 2018) and investigate the role of the prefrontal cortex (Hinaut & Dominey, 2011), known to modulate behavioral activity depending on the context. We propose here to associate our experiences to develop a more general framework for adapting neural networks to problem solving, thus augmenting their usability in AI and the understanding of brain reasoning mechanisms.