{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:35:12Z","timestamp":1740184512962,"version":"3.37.3"},"reference-count":31,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"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":[[2022,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network\u2019s synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate entire neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network commonly used in experimental demonstrations reveals no significant impact of self-discharge on training efficiency. And, even though the network\u2019s weights drift significantly during self-discharge, its predictions remain 100% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse\u2019s current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware.<\/jats:p>","DOI":"10.1088\/2634-4386\/ac9c8a","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:15:02Z","timestamp":1666394102000},"page":"044014","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Reminding forgetful organic neuromorphic device networks"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0065-7144","authenticated-orcid":true,"given":"Daniel","family":"Felder","sequence":"first","affiliation":[]},{"given":"Katerina","family":"Muche","sequence":"additional","affiliation":[]},{"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":[[2022,12,8]]},"reference":[{"article-title":"LaMDA: language models for dialog applications","year":"2022","author":"Thoppilan","key":"nceac9c8abib1"},{"article-title":"Photorealistic text-to-image diffusion models with deep language understanding","year":"2022","author":"Saharia","key":"nceac9c8abib2"},{"key":"nceac9c8abib3","doi-asserted-by":"publisher","first-page":"022501","DOI":"10.1088\/2634-4386\/ac4a83","article-title":"2022 roadmap on neuromorphic computing and engineering","volume":"2","author":"Christensen","year":"2022","journal-title":"Neuromorphic Comput. 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