{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:01:10Z","timestamp":1759968070840,"version":"build-2065373602"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62004223","62304259"],"award-info":[{"award-number":["62004223","62304259"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,6]]},"abstract":"<jats:p>In\u2010sensor computing architecture has a great advantage especially in massive data sampling, transfer, and processing compared with the separated intelligent sensor systems. However, most of the in\u2010sensor computing device is proposed based on the traditional neural network model, where the synapse performs linear multiplication of input and weight. This approach fails to make the most use of the nonlinearity of in\u2010sensor computing devices. Therefore, in this article, first a modified feedforward neural network model with the nonlinear in\u2010sensor computing synapse (NSCS) located at the input layer is presented, and the backpropagation (BP) algorithm is modified to train the network. Then, the nonlinear characteristics of the NSCS composed of the spin\u2010transfer\u2010torque magnetic tunnel junction (STT\u2013MTJ) devices and simple complementary metal\u2010oxide\u2010semiconductor (CMOS) circuit are analyzed. Based on the nonlinear response of STT\u2013MTJ NSCS, the small\u2010scale network with NSCS synapse is experimented on the Modified National Institute of Standards and Technology dataset and compared with the traditional network of the same network size. In the simulation result, it is shown that better performance can be achieved with the STT\u2013MTJ NSCS, including a 2\u201315 times improvement in convergence speed and a 2.5%\u20135.1% increase in accuracy.<\/jats:p>","DOI":"10.1002\/aisy.202300742","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T17:28:26Z","timestamp":1710437306000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spin\u2010Transfer\u2010Torque Magnetic Tunnel Junction Nonlinear In\u2010Sensor Computing Synapse for Improving the Performance of the Feedforward Neural Network"],"prefix":"10.1002","volume":"6","author":[{"given":"Minhui","family":"Ji","sequence":"first","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Jiayuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Liyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Xinmiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Yueguo","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Qingfa","family":"Du","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Jiafei","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Weicheng","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Junping","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Xiaowen","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer National University of Defense Technology  Changsha Hunan 410073 China"}]},{"given":"Yanxiang","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nanodevices and Applications Suzhou Institute of Nano\u2010Tech and Nano\u2010Bionics Chinese Academy of Sciences  Suzhou 215123 P. 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