Data from: Machine learning without a processor: Emergent learning in a nonlinear analog network

<p>The capabilities of digital artificial neural networks grow rapidly with their size, however the time and energy required to train them does as well. The tradeoff is far better for Brains, where the constituent parts (neurons) update their analog connections in ignorance of the actions of other neurons, eschewing centralized processing. Recently introduced analog electronic <em>contrastive local learning networks </em>(CLLNs) share this important decentralized property. However their capabilities were limited because existing implementations are linear. In this dataset we include experimental demonstrations of a nonlinear CLLN, establishing a new paradigm for scalable learning. Included here are data and scripts required to generate figures 2-6 of the manuscript titled "Machine learning without a processor: Emergent learning in a nonlinear analog network".</p>