{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:07:44Z","timestamp":1771474064567,"version":"3.50.1"},"reference-count":15,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Phys.: Conf. Ser."],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Reservoir computing is a versatile approach for implementing physically Recurrent Neural networks which take advantage of a reservoir, consisting of a set of interconnected neurons with temporal dynamics, whose weights and biases are fixed and do not need to be optimized. Instead, the training takes place only at the output layer towards a specific task. One important requirement for these systems to work is nonlinearity, which in optical setups is usually obtained via the saturation of the detection device. In this work, we explore a distinct approach using a photorefractive crystal as the source of the nonlinearity in the reservoir. Furthermore, by leveraging on the time response of the photorefractive media, one can also have the temporal interaction required for such architecture. If we space out in time the propagation of different states, the temporal interaction is lost, and the system can work as an extreme learning machine. This corresponds to a physical implementation of a Feed-Forward Neural Network with a single hidden layer and fixed random weights and biases. Some preliminary results are presented and discussed.<\/jats:p>","DOI":"10.1088\/1742-6596\/2407\/1\/012019","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T18:37:27Z","timestamp":1671129447000},"page":"012019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Reservoir computing with nonlinear optical media"],"prefix":"10.1088","volume":"2407","author":[{"given":"Tiago D.","family":"Ferreira","sequence":"first","affiliation":[]},{"given":"Nuno A.","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Duarte","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Carla C.","family":"Rosa","sequence":"additional","affiliation":[]},{"given":"Ariel","family":"Guerreiro","sequence":"additional","affiliation":[]}],"member":"266","reference":[{"key":"JPCS_2407_1_012019bib1","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1038\/s43588-021-00112-0","article-title":"Scalable optical learning operator","volume":"1","author":"Te\u011fin","year":"2021","journal-title":"Nature Computational Science"},{"key":"JPCS_2407_1_012019bib2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41586-020-2973-6","article-title":"Inference in artificial intelligence with deep optics and photonics","volume":"588","author":"Wetzstein","year":"2020","journal-title":"Nature"},{"key":"JPCS_2407_1_012019bib3","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1587\/nolta.13.26","article-title":"Nonlinear photonic dynamical systems for unconventional computing","volume":"13","author":"Brunner","year":"2022","journal-title":"Nonlinear Theory and Its Applications, IEICE"},{"key":"JPCS_2407_1_012019bib4","doi-asserted-by":"crossref","first-page":"106787","DOI":"10.1016\/j.optlastec.2020.106787","article-title":"A survey of approaches for implementing optical neural networks","volume":"136","author":"Xu","year":"2021","journal-title":"Optics & Laser Technology"},{"key":"JPCS_2407_1_012019bib5","doi-asserted-by":"crossref","first-page":"023013","DOI":"10.1088\/1367-2630\/abda84","article-title":"Reservoir computing with solitons","volume":"23","author":"Silva","year":"2021","journal-title":"New Journal of Physics"},{"key":"JPCS_2407_1_012019bib6","doi-asserted-by":"crossref","first-page":"5564","DOI":"10.1038\/s41467-021-25801-2","article-title":"Next generation reservoir computing","volume":"12","author":"Gauthier","year":"2021","journal-title":"Nature Communications"},{"key":"JPCS_2407_1_012019bib7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTQE.2019.2936281","article-title":"Optical reservoir computing using multiple light scattering for chaotic systems prediction","volume":"26","author":"Dong","year":"2020","journal-title":"IEEE Journal of Selected Topics in Quantum Electronics"},{"key":"JPCS_2407_1_012019bib8","first-page":"6215","article-title":"Random projections through multiple optical scattering: Approximating kernels at the speed of light","author":"Saade","year":"2016"},{"key":"JPCS_2407_1_012019bib9","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1364\/PRJ.423531","article-title":"Photonic extreme learning machine by free-space optical propagation","volume":"9","author":"Pierangeli","year":"2021","journal-title":"Photon. 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