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      Optofluidic memory and self-induced nonlinear optical phase change for reservoir computing in silicon photonics

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          Abstract

          Nanophotonics allows to employ light-matter interaction to induce nonlinear optical effects and realize non-conventional memory and computation capabilities, however to date, light-liquid interaction was not considered as a potential mechanism to achieve computation on a nanoscale. Here, we experimentally demonstrate self-induced phase change effect which relies on the coupling between geometric changes of thin liquid film to optical properties of photonic waveguide modes, and then employ it for neuromorphic computing. In our optofluidic silicon photonics system we utilize thermocapillary-based deformation of thin liquid film capable to induce nonlinear effect which is more than one order of magnitude higher compared to the more traditional heat-based thermo-optical effect, and allowing operation as a nonlinear actuator and memory element, both residing at the same compact spatial region. The resulting dynamics allows to implement Reservoir Computing at spatial region which is approximately five orders of magnitude smaller compared to state-of-the-art experimental liquid-based systems.

          Abstract

          The authors demonstrate optical nonlinear effect based on light-heat-liquid interaction between the geometry of liquid surface and a photonic waveguide mode. They use the liquid as an optical memory to perform nanoscale reservoir computing.

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          Gradient-based learning applied to document recognition

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            Wetting: statics and dynamics

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              Real-time computing without stable states: a new framework for neural computation based on perturbations.

              A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
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                Author and article information

                Contributors
                rubin.shim@gmail.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 July 2023
                21 July 2023
                2023
                : 14
                : 4421
                Affiliations
                [1 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Electrical and Computer Engineering, , University of California, ; San Diego, 9500 Gilman Dr., La Jolla, CA 92093 USA
                [2 ]GRID grid.452562.2, ISNI 0000 0000 8808 6435, King Abdulaziz City for Science and Technology (KACST), ; P.O. Box 6086, Riyadh, 11442 Saudi Arabia
                Author information
                https://orcid.org/http://orcid.org/0000-0003-3561-1664
                https://orcid.org/http://orcid.org/0000-0001-9750-4608
                https://orcid.org/http://orcid.org/0000-0002-4804-4841
                Article
                40127
                10.1038/s41467-023-40127-x
                10362060
                0c779b88-508a-4b04-9414-4bf283de6021
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 November 2022
                : 13 July 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000185, United States Department of Defense | Defense Advanced Research Projects Agency (DARPA);
                Award ID: HR00112090009
                Award Recipient :
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                © Springer Nature Limited 2023

                Uncategorized
                optofluidics,nonlinear optics
                Uncategorized
                optofluidics, nonlinear optics

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