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Found 10 papers, 0 papers with code
Embodiment of Learning in Electro-Optical Signal Processors
Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing.
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals.
Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation
Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals.
Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronisation and cryptography
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.
Large-scale spatiotemporal photonic reservoir computer for image classification
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.
Bayesian optimisation of large-scale photonic reservoir computers
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.
Human action recognition with a large-scale brain-inspired photonic computer
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.
Random pattern and frequency generation using a photonic reservoir computer with output feedback
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.
Online training for high-performance analogue readout layers in photonic reservoir computers
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.
High Speed Human Action Recognition using a Photonic Reservoir Computer
The recognition of human actions in videos is one of the most active research fields in computer vision.
