We demonstrate the first opto-electronic reservoir computer with output feedback and test it on two examples of time series generation tasks: frequency and random pattern generation.
We show numerically that online learning allows to circumvent the added complexity of the analogue layer and obtain the same level of performance as with a digital layer.
We test this approach on a previously reported large-scale experimental system, compare it to the commonly used grid search, and report notable improvements in performance and the number of experimental iterations required to optimise the hyper-parameters.
The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e. g. brain-computer interface and surveillance.
We propose a scalable photonic architecture for implementation of feedforward and recurrent neural networks to perform the classification of handwritten digits from the MNIST database.
We then show that trained reservoir computers can be used to crack chaos based cryptography and illustrate this on a chaos cryptosystem based on the Mackey-Glass system.
Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals.
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals.
Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing.