Towards Adjustable Signal Generation with Photonic Reservoir Computers

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. We have recently reported the first opto-electronic reservoir computer trained online by an FPGA chip. This setup makes it in principle possible to feed the output signal back into the reservoir, which in turn allows to tackle complex prediction tasks in hardware. In present work, we investigate numerically the performance of an offline-trained opto-electronic reservoir computer with output feedback on four signal generation tasks. We report very good results and show the potential of such setup to be used as a high-speed analog control system.

[1]  Benjamin Schrauwen,et al.  Optoelectronic Reservoir Computing , 2011, Scientific Reports.

[2]  L. F. Abbott,et al.  Generating Coherent Patterns of Activity from Chaotic Neural Networks , 2009, Neuron.

[3]  Benjamin Schrauwen,et al.  Phoneme Recognition with Large Hierarchical Reservoirs , 2010, NIPS.

[4]  Geert Morthier,et al.  Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.

[5]  Jochen J. Steil,et al.  Recent advances in efficient learning of recurrent networks , 2009, ESANN.

[6]  Laurent Larger,et al.  Photonic nonlinear transient computing with multiple-delay wavelength dynamics. , 2012, Physical review letters.

[7]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[8]  Auke Jan Ijspeert,et al.  Central pattern generators for locomotion control in animals and robots: A review , 2008, Neural Networks.

[9]  Serge Massar,et al.  All-optical Reservoir Computing , 2012, Optics express.

[10]  Miguel C. Soriano,et al.  Reservoir computing with a single time-delay autonomous Boolean node , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Michiel Hermans,et al.  Towards pattern generation and chaotic series prediction with photonic reservoir computers , 2016, SPIE LASE.

[12]  L Pesquera,et al.  Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. , 2012, Optics express.

[13]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[14]  Michiel Hermans,et al.  Online Training of an Opto-Electronic Reservoir Computer , 2015, ICONIP.

[15]  Benjamin Schrauwen,et al.  Frequency modulation of large oscillatory neural networks , 2014, Biological Cybernetics.

[16]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[17]  Serge Massar,et al.  High performance photonic reservoir computer based on a coherently driven passive cavity , 2015, ArXiv.

[18]  Daniel Brunner,et al.  Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.

[19]  L. Appeltant,et al.  Information processing using a single dynamical node as complex system , 2011, Nature communications.