Digital Brain Seminar

Abstract: Sleep is widely conserved across species and supports multiple essential functions. Its relationship with learning and memory has attracted particular interest: memories are consolidated and reorganized during sleep, while synaptic plasticity also influences sleep architecture. However, the mechanisms and functions linking sleep and synaptic plasticity remain incompletely understood.

In this talk, I will present a computational approach to this problem. First, by analyzing computational models of slow waves, a hallmark of NREM sleep, we found that synaptic potentiation among excitatory neurons can facilitate the generation of slow waves. This prediction was supported by experiments showing that potentiation of excitatory synapses in the prefrontal cortex increased the amount of NREM sleep and slow-wave power. In the second study, I will introduce a biologically plausible model of nonlinear dimensionality reduction. The model can perform computations compatible with t-SNE through three-factor Hebbian plasticity, providing a candidate mechanism for memory generalization during sleep.

Together, these findings suggest a computational framework for understanding how synaptic plasticity contributes to both the mechanisms and functions of sleep.

Abstract: Uncertainty-aware inference from brain recordings is essential for identifying the underlying causal mechanisms of brain (dys)function, thereby advancing precision medicine, eg., through Virtual Brain Twins [1].

In this talk, I will first overview the state-of-the-art probabilistic machine learning approaches for inference, including likelihood-based methods (such as MCMC sampling) and likelihood-free methods (aka simulation-based inference), with the challenges involved in generalization. I will then present our recently developed tools, the flexible Virtual Brain Inference [2] and automatic Dynamic Causal Modeling [3], which are available in the cloud through EBRAINS services.

I will highlight the benefits of incorporating prior knowledge, reparameterization, and inference diagnostics for reliable causal inference. The performance of these methods will be demonstrated in epilepsy, multiple sclerosis, focal intervention, healthy aging, and social facilitation [4]. Finally, I will move beyond model-based inference and discuss learning brain dynamics directly from detailed simulations using deep neural networks. Overall, these results contribute to advances in precision medicine by enhancing the predictive power of Virtual Brain Twins.

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[1] Hashemi et al. "Principles and operation of virtual brain twins." IEEE Reviews in Biomedical Engineering (2025).
[2] Ziaeemehr, et al. "Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models." Elife 14 (2025).
[3] Baldy et al. "Dynamic causal modelling in probabilistic programming languages." Journal of the Royal Society Interface 22.227 (2025).
[4] Esmaeili, et al. "Probabilistic inference of social presence across brain scales reveals enhanced synaptic efficacy." Communications Biology 8.1 (2025): 1608.