Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.

We propose a method for encrypting a signal within the high dimensional chaotic fluctuations of the wavelength from a delayed feedback tunable laser diode. Decoding is performed remotely by using a slave laser diode fully synchronized with the master one. No additional synchronization channel is required. [S0031-9007(98) PACS numbers: 89.70. + c, 05.45. + b Secure communications based on chaos have been investigated for some years, especially in the area of radiofrequency transmissions. Signal encoding and decoding is achieved using a carrier whose amplitude fluctuates chaotically. Compared with conventional data encryption techniques in which the key is a pseudorandom binary number that controls the encryption algorithm, but which is slow, chaos is used as a coding key embodied directly in the structure of the carrier. At least two classical methods have been demonstrated for communicating with chaos [1]. The first method due to Ott, Grebogi, and Yorke [1] utilizes controlling chaos. The dynamics of a chaotic oscillator is made to follow prescribed orbits in the attractor by using small perturbations, thus allowing a message to be encoded in the chaotic wave form. A very different concept developed by Pecora and Carroll [1] uses the idea of synchronized chaos for secure communications. In this case, a small information-bearing signal is masked by a large chaotic signal. The chaotic codegenerating system is divided in two subsystems, namely, the master and the slave. The slave is replicated at the receiver. The master subsystem is used to synchronize the two slave subsystems. The message signal is added to the chaotic signal generated by the slave subsystem at the transmitter and this composite signal is transmitted to the receiver. When the two subsystems are synchronized, the message can be reproduced by subtracting the chaotic part of the composite signal. So far most of the studies were implemented with electrical circuits featuring low dimensional attractors, such as the double scroll or Chua's attractor that has a single positive Lyapunov exponent [2]. The simple chaotic processes thus obtained can, however, be defeated by an eavesdropper without the synchronized receiver, using unmasking signal processing techniques which work well for simple chaotic systems [3]. One way to solve this security problem is by using hyperchaotic systems with multiple positive Lyapunov exponents to mask the message. A recent theoretical work [4] indicates the possibility of synchronizing hyperchaotic chaos with just one transmitted signal. Practical realizations remain, however, to be developed. The question of optical synchronization of chaos has also been studied in optics, but results deal mostly with numerical simulations [5] except some unique demonstrations of control of laser chaos and digital encoded transmission [6]. When talking about chaos in optics, it looks natural to look for nonlinearities induced by optical power. It is probably the reason why implementing optical chaotic cryptosystems poses severe problems...

Photonic waveguides are prime candidates for integrated and parallel photonic interconnects. Such interconnects correspond to large-scale vector matrix products, which are at the heart of neural network computation. However, parallel interconnect circuits realized in two dimensions, for example, by lithography, are strongly limited in size due to disadvantageous scaling. We use three-dimensional (3D) printed photonic waveguides to overcome this limitation. 3D optical couplers with fractal topology efficiently connect large numbers of input and output channels, and we show that the substrate's area and height scale linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.

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