Projects and Open Source
Selected tools and repositories that support reproducible neuroscience and simulation-based inference.
Focus
Neural modeling, inference workflows, simulation pipelines, and teaching toolkits.
Softwares
The Virtual Brain Inference (VBI) Toolkit
The Virtual Brain Inference (VBI) toolkit is an open-source, flexible solution tailored for probabilistic inference on virtual brain models. It integrates computational models with personalized anatomical data to deepen the understanding of brain dynamics and neurological processes.
VBI supports fast simulations, comprehensive feature extraction, and employs deep neural density estimators to handle various neuroimaging data types. Its goal is to bridge the gap in solving the inverse problem of identifying control parameters that best explain observed data, thereby making these models applicable for clinical settings. VBI leverages high-performance computing through GPU acceleration and C++ code to ensure efficiency in processing.
Research Papers Repositories
SBI_VBM
Hashemi, M., Ziaeemehr, A., Woodman, M.M., Fousek, J., Petkoski, S. and Jirsa, V., 2024. Simulation-based inference on virtual brain models of disorders. Machine Learning: Science and Technology.
Frontiers 2021
Ziaeemehr, A. and Valizadeh, A., 2021. Frequency-resolved functional connectivity: role of delay and the strength of connections. Frontiers in Neural Circuits, 15, p.608655.
Neural Network 2020
Ziaeemehr, A., Zarei, M., Valizadeh, A., and Mirasso, C.R. (2020). Frequency-dependent organization of the brain’s functional network through delayed-interactions. Neural Networks, 132, 155–165.
Scientific Reports 2020
Ziaeemehr, A., Zarei, M. and Sheshbolouki, A., 2020. Emergence of global synchronization in directed excitatory networks of type I neurons. Scientific Reports, 10(1), p.3306.
Parkinson Modeling
Implementations of basal ganglia spiking and rate models for Parkinson disease.
Books Code & Packages
netsci
Code for Network Science by Albert-László Barabási (2016).
spikes
Spikes, Decisions, and Actions: The Dynamical Foundations of Neuroscience, Wilson (1999).
jitcsim
Simulation of complex network dynamics with Ordinary/Delay/Stochastic differential equations (just-in-time compilation).
ModelingNeuralDynamics
Python/Brian codes for An Introduction to Modeling Neuronal Dynamics by Christoph Borgers.
mndynamics
Standalone Python package for An Introduction to Modeling Neuronal Dynamics by Christoph Borgers.
vbjax
A nascent Jax-based package for virtual brain modeling.