{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T22:37:41Z","timestamp":1784673461441,"version":"3.55.0"},"reference-count":59,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["18-79-10253"],"award-info":[{"award-number":["18-79-10253"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:sec><jats:label\/><jats:p>Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle\u2010to\u2010cycle, c2c, or device\u2010to\u2010device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain\u2010inspired computing systems built with partially unreliable analog elements.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200407","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T02:49:51Z","timestamp":1677725391000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Combination of Organic\u2010Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification"],"prefix":"10.1002","volume":"5","author":[{"given":"Anna N.","family":"Matsukatova","sequence":"first","affiliation":[{"name":"National Research Centre Kurchatov Institute  123182 Moscow Russia"},{"name":"Department of Physics Lomonosov Moscow State University  119991 Moscow 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