{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:32:01Z","timestamp":1772137921812,"version":"3.50.1"},"reference-count":31,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"tdm","delay-in-days":7,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["WE 4678\/12-1"],"award-info":[{"award-number":["WE 4678\/12-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Neuromorph. Comput. Eng."],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Organic neuromorphic devices can accelerate neural networks and integrate with biological systems. Devices based on the biocompatible and conductive polymer PEDOT:PSS are fast, require low amounts of energy and perform well in crossbar simulations. However, parasitic electrochemical reactions lead to self-discharge and the fading of the learned conductance states over time. This limits a neural network\u2019s operating time and requires complex compensation mechanisms. Spiking neural networks (SNNs) take inspiration from biology to implement local and always-on learning. We show that these SNNs can function on organic neuromorphic hardware and compensate for self-discharge by continuously relearning and reinforcing forgotten states. In this work, we use a high-resolution charge transport model to describe the behavior of organic neuromorphic devices and create a computationally efficient surrogate model. By integrating the surrogate model into a Brian 2 simulation, we can describe the behavior of SNNs on organic neuromorphic hardware. A biologically plausible two-layer network for recognizing\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mn>28<\/mml:mn>\n                        <mml:mo>\u00d7<\/mml:mo>\n                        <mml:mn>28<\/mml:mn>\n                      <\/mml:math>\n                      <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"nceaccd90ieqn1.gif\" xlink:type=\"simple\"\/>\n                    <\/jats:inline-formula>\n                    pixel MNIST images is trained and observed during self-discharge. The network achieves, for its size, competitive recognition results of up to 82.5%. Building a network with forgetful devices yields superior accuracy during training with 84.5% compared to ideal devices. However, trained networks without active spike-timing-dependent plasticity quickly lose their predictive performance. We show that online learning can keep the performance at a steady level close to the initial accuracy, even for idle rates of up to 90%. This performance is maintained when the output neuron\u2019s labels are not revalidated for up to 24 h. These findings reconfirm the potential of organic neuromorphic devices for brain-inspired computing. Their biocompatibility and the demonstrated adaptability to SNNs open the path towards close integration with multi-electrode arrays, drug-delivery devices, and other bio-interfacing systems as either fully organic or hybrid organic-inorganic systems.\n                  <\/jats:p>","DOI":"10.1088\/2634-4386\/accd90","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T18:28:05Z","timestamp":1681756085000},"page":"024008","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Spiking neural networks compensate for weight drift in organic neuromorphic device networks"],"prefix":"10.1088","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0065-7144","authenticated-orcid":true,"given":"Daniel","family":"Felder","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8556-9217","authenticated-orcid":false,"given":"John","family":"Linkhorst","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7874-5315","authenticated-orcid":true,"given":"Matthias","family":"Wessling","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"nceaccd90bib1","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1038\/s41928-018-0103-3","article-title":"Organic electronics for neuromorphic computing","volume":"1","author":"van de Burgt","year":"2018","journal-title":"Nat. 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