Tag: brain

« Apprentissage continu et contrôle cognitif » (Alexandre)

Frédérique Alexandre , Inria, Bordeaux, 14-Dec  2023

Résumé : J’explore la différence entre l’efficacité de l’apprentissage humain et celle des grands modèles de langage en termes de temps de calcul et de coûts énergétiques. L’étude se focalise sur le caractère continu de l’apprentissage humain et les défis associés, tels que l’oubli catastrophique. Deux types de mémoires, la mémoire de travail et la mémoire épisodique, sont examinés. Le cortex préfrontal est décrit comme essentiel pour le contrôle cognitif et la mémoire de travail, tandis que l’hippocampe est central pour la mémoire épisodique. Alexandre suggère que ces deux régions collaborent pour permettre un apprentissage continu et efficace, facilitant ainsi la pensée et l’imagination.

Abstract: I explore the difference between the efficiency of human learning and that of large language models in terms of computational time and energy costs. The study focuses on the continuous nature of human learning and associated challenges, such as catastrophic forgetting. Two types of memory, working memory and episodic memory, are examined. The prefrontal cortex is described as essential for cognitive control and working memory, while the hippocampus is central for episodic memory. Alexandre suggests that these two regions collaborate to enable continuous and effective learning, thus facilitating thought and imagination. 

Frédéric Alexandre est directeur de recherche à l’Inria et dirige l’équipe Mnemosyne à Bordeaux, spécialisée en Intelligence Artificielle et Neurosciences Computationnelles. L’équipe étudie les différentes formes de mémoire cérébrale et leur rôle dans des fonctions cognitives telles que le raisonnement et la prise de décision. Ils explorent la dichotomie entre mémoires explicites et implicites et comment elles interagissent. Leurs projets récents s’étendent de l’acquisition du langage à la planification et la délibération. Les modèles créés sont validés expérimentalement et ont des applications médicales, industrielles, ainsi qu’en sciences humaines, notamment en éducation, droit, linguistique, économie, et philosophie.

Frédéric Alexandre. A global framework for a systemic view of brain modeling. Brain Informatics, 2021, 8 (1), 

Snigdha Dagar, Frédéric Alexandre, Nicolas P. Rougier. From concrete to abstract rules : A computational sketch. 15th International Conference on Brain Informatics, Jul 2022.  

Randa Kassab, Frédéric Alexandre. Pattern Separation in the Hippocampus: Distinct Circuits under Different Conditions. Brain Structure and Function, 2018, 223 (6), pp.2785-2808. 

Hugo Chateau-Laurent, Frédéric Alexandre. The Opportunistic PFC: Downstream Modulation of a Hippocampus-inspired Network is Optimal for Contextual Memory Recall. 36th Conference on Neural Information Processing System, Dec 2022.  

Pramod Kaushik, Jérémie Naudé, Surampudi Bapi Raju, Frédéric Alexandre. A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning. Neurobiology of Learning and Memory, 2022, 193 (107653),  

Constraining networks biologically to explain grounding (Pulvermüller)

Friedemann Pulvermueller, FU Berlin, 3 December, 2020

VIDEO

Abstract: Meaningful use of symbols requires grounding in action and perception through learning. The mechanisms of this sensorimotor grounding, however, are rarely specified in mechanistic terms; and mathematically precise formal models of the relevant learning processes are scarce. As the brain is the device that is critical for mechanistically supporting and indeed implementing grounding, modelling needs to take into account realistic neuronal processes in the human brain. This makes it desirable to use not just ‘neural’ networks that are vaguely similar to some aspects of real networks of neurons, but models implementing constraints imposed by neuronal structure and function, that is, biologically realistic learning and brain structure along with local and global structural connectivity and functional interaction. After discussing brain constraints for cognitive modelling, the talk will focus on the biological implementation of grounding, in order to address the following questions: Why do the brains of humans — but not those of their closest relatives — allow for verbal working memory and learning of huge vocabularies of symbols? Why do different word and concept types seem to depend on different parts of the brain (‘category-specific’ semantic mechanisms)? Why are there ‘semantic and conceptual hubs’ in the brain where general semantic knowledge is stored — and why would these brain areas be different from those areas where grounding information is present (i.e., the sensory and motor cortices)? And why should sensory deprivation shift language and conceptual processing toward ‘grounding areas’ — for example toward the visual cortex in the blind? I will argue that brain-constrained modelling is necessary to answer (some of) these questions and, more generally, to explain the mechanisms of grounding. 

Friedemann Pulvermüller is professor in the neuroscience of language and pragmatics at the Freie Universität Berlin, where he also directs the ‘Brain Language Laboratory’. 

Carota, F., Nili, H., Kriegeskorte, N., & Pulvermüller, F. (2023). Experientially-grounded and distributional semantic vectors uncover dissociable representations of conceptual categories. Language, Cognition and Neuroscience, 1-25.

Pulvermüller, F., Garagnani, M., & Wennekers, T. (2014). Thinking in circuits: Towards neurobiological explanation in cognitive neuroscience. Biological Cybernetics, 108(5), 573-593. doi: 10.1007/s00422-014-0603-9