Stochastic model improves brain activity simulation across scales
Towards a faithful stochastic model for brain digital twins
Software Engineering
Summary
Simulating the brain’s electrical activity accurately is difficult because it happens at many levels, from tiny neurons to large brain regions. Existing models usually focus on just one level, either small or large, but not both with high fidelity. The authors propose a new model that combines two mathematical methods to better mimic this activity across levels. They also turned this model into an initial simulation that could be used in digital brain replicas called brain digital twins.
What this means in practice
- •For medical device developers: Create more precise brain activity simulators that span from individual neuron behavior to brain regions for better medical device testing.
- •For software engineers building brain-computer interfaces: Integrate a multi-scale stochastic brain model to improve the responsiveness and accuracy of brain-computer interfaces during development.
Authors
Abdelhamid Rouatbi, Alexandre Muzy, Eugene Syriani
Abstract
Twinning the brain means reproducing its electrical activity with fidelity. This activity arises from many neuronal mechanisms across several scales, from a single neuron to whole brain regions. Current large initiatives rely on models that each capture only a few of these mechanisms. Some act at the scale of a single neuron, others at the scale of neuronal populations. No single model captures both scales with fidelity. We propose a new stochastic model, based on a combination of Random Neural Networks and Markov-Modulated Poisson Processes, that would improve the fidelity of a brain digital twin. We map the model to the Discrete Event System Specification. This yields an initial executable simulation model suitable for incorporation into a brain digital twin.