Neural - AI II

Power grid networks, as well as neuronal networks with synaptic plasticity, describe real-world systems of tremendous importance for our daily life. We provide insight into the fundamental relation between these two types of networks by proving that phase oscillator models with inertia can be viewed as a particular class of adaptive networks. As an immediate consequence of the unification, we find a novel type of multicluster state for phase oscillators with inertia and the emergence of solitary nodes (see also Figure 1).

The complex processes of learning and memory in vivo often involve simultaneous alterations in synaptic strength and intrinsic excitability of the constituent neurons. However, such synergy is rarely explicitly manifested in the bottom-up training of spiking neural models, partially because of conceptual and computational difficulties.

In classical physics and computer science, cellular automata (CA) provide a powerful framework for investigating the emergence of large-scale complex structures from local dynamical rules [1]. Similarly, complex dynamics of quantum many body systems have been studied using Quantum Cellular Automata (QCA) [2], in which the cellular update rules are implemented by localizable unitary maps.