We apply time dependent spectral phase modulation to generate pulse trains that are spectrally and temporally partially coherent in an ensemble averaged sense. We consider, in particular, quadratic spectral phase modulation of Gaussian pulses, and demonstrate two particular types of nonuniformly correlated pulse trains. The controlled partial temporal coherence of the nonstationary fields is generated using a pulse compressor and experimentally verified with frequency resolved optical gating (FROG). We show that the correlation characteristics of such pulse trains can be retrieved directly from the FROG spectrograms provided one has certain a priori knowledge of the pulse train. Our results open a pathway for experimental confirmation of several correlation induced effects in the temporal domain.
An efficient photonic hardware integration of neural networks can benefit us from the inherent properties of parallelism, high-speed data processing and potentially low energy consumption. In artificial neural networks (ANN), neurons are classified as static, single and continuous-valued. On contrary, information transmission and computation in biological neurons occur through spikes, where spike time and rate play a significant role. Spiking neural networks (SNNs) are thereby more biologically relevant along with additional benefits in terms of hardware friendliness and energy-efficiency. Considering all these advantages, we designed a photonic reservoir computer (RC) based on photonic recurrent spiking neural networks (SNN) i.e. a liquid state machine. It is a scalable proof-of-concept experiment, comprising more than 30,000 neurons. This system presents an excellent testbed for demonstrating next generation bio-inspired learning in photonic systems.
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