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The brain as an optimal efficient adaptive learner
mardi 16 mai 2017

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Descriptif

Conférence de Sophie Deneve lors du colloquium Data Science Colloquium  de l'ENS.

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level.

 

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Auteur(s)
Sophie Deneve
Ecole normale supérieure
Chercheur

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Institutions : Ecole normale supérieure

Cursus :

Sophie Deneveest chercheur CNRS dans l'équipe "Group for Neural Theory" au Laboratoire de Neurosciences Cognitives, Département d'études cognitives à l'Ecole normale supérieure.

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Dernière mise à jour : 04/07/2017