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Comput. Eng."],"published-print":{"date-parts":[[2024,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Designing compact computing hardware and systems is highly desired for resource-restricted edge computing applications. Utilizing the rich dynamics in a physical device for computing is a unique approach in creating complex functionalities with miniaturized footprint. In this work, we developed a dynamical electrochemical memristor from a static memristor by replacing the gate material. The dynamical device possessed short-term fading dynamics and exhibited distinct frequency-dependent responses to varying input signals, enabling its use as a single device-based frequency classifier. Simulation showed that the device responses to different frequency components in a mixed-frequency signal were additive with nonlinear attenuation at higher frequency, providing a guideline in designing the system to process complex signals. We used a rate-coding scheme to convert real world auditory recordings into fixed amplitude spike trains to decouple amplitude-based information and frequency-based information and was able to demonstrate auditory classification of different animals. The work provides a new building block for temporal information processing.<\/jats:p>","DOI":"10.1088\/2634-4386\/ad33cc","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T22:20:35Z","timestamp":1710454835000},"page":"014012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Neuromorphic auditory classification based on a single dynamical electrochemical memristor"],"prefix":"10.1088","volume":"4","author":[{"given":"Peng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xuehao","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Bihua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuxuan","family":"Ye","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-6181","authenticated-orcid":true,"given":"Gang","family":"Pan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0679-8063","authenticated-orcid":true,"given":"Peng","family":"Lin","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ncead33ccbib1","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","article-title":"Towards spike-based machine intelligence with neuromorphic computing","volume":"575","author":"Roy","year":"2019","journal-title":"Nature"},{"key":"ncead33ccbib2","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"article-title":"Neural turing machines","year":"2014","author":"Graves","key":"ncead33ccbib3"},{"key":"ncead33ccbib4","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. 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