GELIFES Seminars - Guillaume Etter

Guillaume Etter (GELIFES)

How to build a brain?

Unlike computers that can store information in a dedicated location, memories in biological systems are stored in large complex networks of highly distributed synapses, the points of contact between neurons. Understanding learning in biological circuits is thus highly challenging: how to determine which synapses should be updated to improve behaviors? How to apply synaptic updates to support the acquisition of new knowledge while preserving (instead of overwriting) old memories? This presentation will highlight recent advances in our understanding of learning and memory using state-of-the-art all-optical in vivo approaches to simultaneously record and manipulate thousands of neurons in real time in freely moving animals. These learning principles are also evaluated during the sleep-wake cycle and in Alzheimer's disease conditions. Finally, I posit that to understand the brain and its fundamental principles, one must build it. By leveraging deep artificial neural networks that power today's AI systems, I will share a recent brain-inspired model that integrates fundamental biological principles and recapitulates some aspects of human cognition.

Biosketch:
My research lies at the intersection of neuroscience and artificial intelligence and focuses on learning and memory in neural networks. I use neurophysiological approaches (in vivo calcium imaging, electrophysiology, optogenetics) and brain-inspired deep learning models. I hold a PhD in Neuroscience from the University of Strasbourg supported by scholarships from the Ministry of Research and Education as well as the Fondation de La Recherche Médicale, where I investigated dopaminergic signaling in memory using patch-clamp electrophysiology. I received postdoctoral training at McGill University (Canada) where I developed open source miniaturized head-mounted microscopes, optogenetics and in vivo electrophysiology, supported by a foundation grant and a project grant from the Canadian Institute of Health Research. I then joined the Montreal Institute of Learning Algorithms (MILA) as a researcher in AI where I developed brain-inspired models of cortical computations. I joined GELIFES in 2025 as a researcher and currently focus on understanding how biological neural networks incrementally accumulate knowledge over a lifetime.