Neuromorphic computing hardware that requires conventional training procedures based on backpropagation is difficult to scale, because of the need for full observability of network states and for programmability of network parameters. Therefore, the search for hardware-friendly and biologically-plausible learning schemes, and suitable platforms, is pivotal for the future developments of the field. We present a novel experimental study of a photonic integrated neural network featuring rich recurrent nonlinear dynamics and both short- and long-term plasticity. Scalability in these architectures is greatly enhanced by the capability to process input and to generate output that are encoded concurrently in the temporal, spatial and wavelength domains. Moreover, we discuss a novel biologically-plausible, backpropagation-free and hardware-friendly learning procedure based on our neuromorphic hardware.
In this work, we study the eigenvalues and eigenvectors of a taiji microresonator that is made by a ring cavity with an embedded S-shaped waveguide. Specifically, we demonstrated that this system works on an exceptional point. This strongly affects the behaviour against perturbation of the counterpropagating modes inducing a peculiar splitting of the single Lorentzian transmission spectra. Precisely, we show that this splitting is proportional to the square root of the perturbation strength. Therefore, the taiji exhibits a higher sensitivity to a small perturbation with respect to a typical microresonator which shows a linear splitting.
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