Learning Without a Global Clock: Asynchronous Learning in a Physics-Driven Learning Network. (arXiv:2201.04626v1 [cond-mat.soft])

In a neuron network, synapses update individually using local information,
allowing for entirely decentralized learning. In contrast, elements in an
artificial neural network (ANN) are typically updated simultaneously using a
central processor. Here we investigate the feasibility and effect of
asynchronous learning in a recently introduced decentralized, physics-driven
learning network. We show that desynchronizing the learning process does not
degrade performance for a variety of tasks in an idealized simulation. In
experiment, desynchronization actually improves performance by allowing the
system …

arxiv global learning network physics