Research Reveals How to Optimize Neural Networks on a Brain-Inspired Computer

Neural networks in both biological settings and artificial intelligence distribute computation across their neurons to solve complex tasks. New research now shows how so-called "critical states" can be used to optimize artificial neural networks running on brain-inspired neuromorphic hardware. The study was carried out by scientists from Heidelberg University working within the Human Brain Project, and the Max-Planck-Institute for Dynamics and Self-Organization (MPIDS). The results have been published in Nature Communications. Many computational properties are maximized when the dynamics of a network are at a "critical point", a state where systems can quickly change their overall characteristics in fundamental ways, transitioning e.g. between order and chaos or stability and instability. Therefore, the critical state is widely assumed to be optimal for any computation in recurrent neural networks, which are used in many AI applications.