Damien Querlioz is a CNRS Research Director at the Centre de Nanosciences et de Nanotechnologies of Université Paris-Saclay and CNRS. His research focuses on novel usages of emerging non-volatile memory and other nanodevices, in particular relying on inspirations from biology and machine learning. He received his predoctoral education at Ecole Normale Supérieure, Paris and his PhD from Université Paris-Sud in 2009. Before his appointment at CNRS, he was a Postdoctoral Scholar at Stanford University and at the Commissariat à l’Energie Atomique. Damien Querlioz is the coordinator of the interdisciplinary INTEGNANO research group, with colleagues working on all aspects of nanodevice physics and technology, from materials to systems. In 2016, he was the recipient of an ERC Starting Grant to develop the concept of natively intelligent memory. In 2017, he received the CNRS Bronze medal. He has also been a co-recipient of the 2017 IEEE Guillemin-Cauer Best Paper Award and of the 2018 IEEE Biomedical Circuits and Systems Best Paper Award.

Title: Noisy Synapses, Reliable Learning: Turning Uncertainty into a Neuromorphic Resource

Biological synapses do more than change their strength: they also regulate how changeable they should remain. This “plasticity of plasticity,” or metaplasticity, is thought to help neural systems balance memory retention with flexibility. While the underlying biological mechanisms remain only partly understood, this idea offers a powerful inspiration for neuromorphic AI: a useful learning system should not only update its synapses, but also decide which synapses should stay plastic, which should consolidate, which information should fade, and when new evidence is worth acquiring.

This talk develops that idea through a Bayesian view of metaplasticity. The central proposal is that uncertainty can act as a local synaptic control signal. Rather than treating uncertainty as an external diagnostic computed after training, we embed it into the learning rule itself. Uncertain synapses remain adaptable, confident synapses are protected from unnecessary change, and outdated information can relax through controlled forgetting. This creates a bridge between bio-inspired principles, continual learning, and neuromorphic hardware constraints, where memory is finite, synaptic writes are costly, and stochasticity is unavoidable.

I will first introduce MESU, Metaplasticity from Synaptic Uncertainty [1]. Inspired by the idea that synapses may carry not only a weight but also an “error bar” on that weight, MESU derives a continual-learning rule in which each parameter update is scaled by synaptic uncertainty. A bounded-memory Bayesian formulation adds controlled forgetting, allowing networks to avoid both catastrophic forgetting and excessive rigidity, without relying on explicit task boundaries. MESU therefore turns synaptic uncertainty into a mechanism for balancing plasticity, consolidation, and adaptation.

I will then present BiMU, Binary Metaplasticity from Uncertainty [2]. BiMU brings the same principle to binary Bayesian neural networks, where the connection to neuromorphic systems is especially direct: synapses have minimal precision, updates are expensive, and stochastic sampling is cheap. By preventing binary posterior saturation, BiMU keeps synapses from becoming permanently frozen and preserves useful epistemic uncertainty. This uncertainty can then drive active continual learning: the system requests labels and performs updates only when stochastic network samples disagree.

Together, MESU and BiMU suggest a neuromorphic view of learning in which finite precision, stochasticity, and forgetting are not necessarily limitations, but computational resources.

[1]  D. Bonnet, K. Cottart, T. Hirtzlin, T. Januel, T. Dalgaty, E. Vianello, D. Querlioz, “Bayesian continual learning and forgetting in neural networks”, Nature Communications, 16(1), 9614 (2025).

[2] K. Cottart, T. Ballet, D. Bonnet, D. Querlioz,  “Active Continual Learning with Metaplastic Binary Bayesian Neural Networks”, ICML, 2026.