Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. A recent paper published by SP6 partners presents a mathematical framework and a model based on a graph-theoretical approach that, starting from sparse experimental data at the cellular level, can quantitatively explain and predict the corresponding full network properties. The model changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system.

Further reading

• Read the full research article here.
• View the live paper here.