Multi-scale models of the rat and mouse brain integrate models of ion channels, single cells, microcircuits, brain regions, and brain systems at different levels of granularity (molecular models, morphologically detailed cellular models, and abstracted point neuron models).
Blue Brain’s reconstruction and related simulations are made possible by a comprehensive software ecosystem for each step in the reconstruction and simulation process. Blue Brain systematically releases open source software.
The online tools available to assist users with simulation neuroscience are a collection of platforms and atlases from the Blue Brain and our collaborators.
Data is of vital importance to the study of the Brain. For neuroscientists in this section, there are ion channel recordings, morphological reconstructions, electrical recordings from neocorticol neurons and molecular properties of neurons. These are available on Blue Brain platforms and the Human Brain Project Brain Simulation platform.
The EPFL Blue Brain Project has built the first next-generation models of thalamocortical neurons. These digital models of thalamocortical neurons were built using state-of-the art optimization techniques, which directly constrain unknown parameter values with experimental data. Thalamocortical neurons are essential components in the transmission of information from the outside world to higher order brain areas, such as the neocortex. These neurons fire action potentials in distinct modes, which are associated with different brain states, such as wakefulness and sleep. These findings are the first phase towards the complete modelling of the rodent thalamus, which is the next step for the Blue Brain Project. In addition, the experimental and modelling community can now use these data and models in their analysis and modelling workflows.
How do neurons process information? Neurons are known to break down an incoming electrical signal into sub-units. Now, researchers at Blue Brain have discovered that dendrites, the neuron’s tree-like receptors, work together – dynamically and depending on the workload – for learning. The findings further our understanding of how we think and may inspire new algorithms for artificial intelligence.