By a News Reporter-Staff News Editor at Education Letter -- Researchers detail new data in Neural Networks and Learning Systems. According to news reporting originating in Ghent, Belgium, by VerticalNews journalists, research stated, "Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation."
The news reporters obtained a quote from the research from Ghent University, "These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur."
According to the news reporters, the research concluded: "Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)."
For more information on this research see: Nanophotonic Reservoir Computing With Photonic Crystal Cavities to Generate Periodic Patterns. IEEE Transactions on Neural Networks and Learning Systems, 2014;25(2):344-355. IEEE Transactions on Neural Networks and Learning Systems can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Neural Networks and Learning Systems - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72)
Our news correspondents report that additional information may be obtained by contacting M.A.A. Fiers, University of Ghent, ELIS Department, B-9000 Ghent, Belgium. Additional authors for this research include T. Van Vaerenbergh, F. Wyffels, D. Verstraeten, B. Schrauwen, J. Dambre and P. Bienstman.
Keywords for this news article include: Ghent, Europe, Belgium, Neural Networks and Learning Systems
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