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Validation and inference

Advanced tools for analysing, inferring, and validating models against various datasets, scales, and species.

EBRAINS offers cutting-edge tools for scientists, including a model validation web service, Python libraries based on Elephant for statistical analysis of electrophysiological data, Frites for information-theoretic analysis and network-level statistics, and TVB-Inverse for Monte Carlo simulations in a Bayesian framework.


This tool offers powerful methods that enable principled solutions to complex inverse problems and statistical inference at meso to macro scales, using invasive and non-invasive recordings. It leverages Bayesian inference to uncover the posterior distribution of TVB's model parameters at the whole-brain level, such as the spatial map of excitability or the degree of degradation in a personalised connectome.

TVB-inverse combines fast simulations (such as JAX, JIT, and C++) of virtual brains with state-of-the-art Monte Carlo sampling and probabilistic AI/ML algorithms, enabling reliable, efficient, and flexible causal inference at the whole-brain scale in both CPU and GPU. With TVB-inverse, scientists can efficiently perform causal inference when the experiments are difficult or impossible, making it an essential tool for researchers in neuroscience with clinical applications.

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