Desynchronous and decentralized learning In a physical network

Previous work has realized what we call a physics-driven learning network: an electrical network of variable resistors capable of learning a variety of tasks using simple local rules and natural power minimization [1]. Like biological neuron networks, this network performs distributed computation using exclusively local information, and thus is scalable and robust to damage. However, it uses a global clock to update each edge simultaneously. Here, we take the system one step closer to its biological inspiration by relaxing the global clock requirement, allowing individual edges of the network to update randomly and independently. We find that this stochasticity does not impair performance, and can even improve the learning error for some tasks. This effect can be understood by analogy to Stochastic Gradient Descent. Our results suggest that our approach can not only be implemented in metamaterials or other sensors where a central clock is untenable, but can also improve learning.

[1] S. Dillavou et. al. ArXiv (2021) https://arxiv.org/abs/2108.00275