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Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated howdogs can learn to associate a ringing bell with food, thereby causing a ring toresult in salivation. Today, it is rare to find the use of Pavlovian typeassociative learning for artificial intelligence (AI) applications even thoughother learning concepts, in particular backpropagation on artificial neuralnetworks (ANNs) have flourished. However, training using the backpropagationmethod on 'conventional' ANNs, especially in the form of modern deep neuralnetworks (DNNs), is computationally and energy intensive. Here weexperimentally demonstrate a form of backpropagation-free learning using asingle (or monadic) associative hardware element. We realize this on anintegrated photonic platform using phase-change materials combined with on-chipcascaded directional couplers. We then develop a scaled-up circuit networkusing our monadic Pavlovian photonic hardware that delivers a distinctmachine-learning framework based on single-element associations and,importantly, using backpropagation-free architectures to address generallearning tasks. Our approach reduces the computational burden imposed bylearning in conventional neural network approaches, thereby increasing speed,whilst also offering higher bandwidth inherent to our photonic implementation.

Monadic Pavlovian associative learning in a backpropagation-free photonic network