Viktor Jirsa

Michael Schirner, Lia Domide, Dionysios Perdikis et al.

The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science. It offers services for constructing, simulating and analysing brain network models (BNMs) including the TVB network simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional connectomes; multiscale co-simulation of spiking and large-scale networks; a domain specific language for automatic high-performance code generation from user-specified models; simulation-ready BNMs of patients and healthy volunteers; Bayesian inference of epilepsy spread; data and code for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability and clinical translation.

Viktor Sip, Martin Breyton, Spase Petkoski et al.

Learning stochastic models of dynamical systems underlying observed data is of interest in many scientific fields. Here we propose a novel method for this task, based on the framework of variational autoencoders for dynamical systems. The method estimates from the data both the system state trajectories and noise time series. This approach allows to perform multi-step system evolution and supports a teacher forcing strategy, alleviating limitations of autoencoder-based approaches for stochastic systems. We demonstrate the performance of the proposed approach on six test problems, covering simulated and experimental data. We further show the effects of the teacher forcing interval on the nature of the internal dynamics, and compare it to the deterministic models with equivalent architecture.