  Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how
dogs can learn to associate a ringing bell with food, thereby causing a ring to
result in salivation. Today, it is rare to find the use of Pavlovian type
associative learning for artificial intelligence (AI) applications even though
other learning concepts, in particular backpropagation on artificial neural
networks (ANNs) have flourished. However, training using the backpropagation
method on 'conventional' ANNs, especially in the form of modern deep neural
networks (DNNs), is computationally and energy intensive. Here we
experimentally demonstrate a form of backpropagation-free learning using a
single (or monadic) associative hardware element. We realize this on an
integrated photonic platform using phase-change materials combined with on-chip
cascaded directional couplers. We then develop a scaled-up circuit network
using our monadic Pavlovian photonic hardware that delivers a distinct
machine-learning framework based on single-element associations and,
importantly, using backpropagation-free architectures to address general
learning tasks. Our approach reduces the computational burden imposed by
learning in conventional neural network approaches, thereby increasing speed,
whilst also offering higher bandwidth inherent to our photonic implementation.
