Optimising Complex Networks on a Neuromorphic Computer System

© Benjamin Cramer, Kirchhoff-Institut für Physik - Like biological systems, artificial neuronal networks distribute computations across their interconnected neurons to solve complex tasks. Scientists from Heidelberg University and the Max Planck Institute for Dynamics and Self-Organization in Göttingen studied how so-called critical states can be used to optimise these networks. They used a prototype of the BrainScaleS-2 system developed by Heidelberg physicists within the framework of the Human Brain Project. This neuromorphic computer architecture is oriented to the structure of the human brain. The results of this research were published in the journal "Nature Communications". Complex networks develop a number of special properties when posed at a "critical point", a state where systems can quickly change their fundamental behaviour, transitioning e.g. between order and chaos or stability and instability. Many computational characteristics are optimised in this state, hence criticality is widely assumed to be optimal for any computation in recurrent neural networks.