{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:09:20Z","timestamp":1777486160952,"version":"3.51.4"},"reference-count":40,"publisher":"MIT Press - Journals","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:p> Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large ( N = 134\u2013402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients. <\/jats:p>","DOI":"10.1162\/neco_a_01189","type":"journal-article","created":{"date-parts":[[2019,4,13]],"date-time":"2019-04-13T00:05:57Z","timestamp":1555113957000},"page":"1085-1113","source":"Crossref","is-referenced-by-count":50,"title":["Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network"],"prefix":"10.1162","volume":"31","author":[{"given":"Po-He","family":"Tseng","sequence":"first","affiliation":[{"name":"Department of Neurobiology and Duke University Center for Neuroengineering, Duke University, Durham, NC 27710, U.S.A."}]},{"given":"N\u00faria Armengol","family":"Urpi","sequence":"additional","affiliation":[{"name":"Departments of Information and Communication Technologies and Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, 08018, Spain; and Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland"}]},{"given":"Mikhail","family":"Lebedev","sequence":"additional","affiliation":[{"name":"Department of Neurobiology and Duke University Center for Neuroengineering, Duke University, Durham, NC 27710, U.S.A.; and Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia; and Department of Information and Internet Technologies of Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia"}]},{"given":"Miguel","family":"Nicolelis","sequence":"additional","affiliation":[{"name":"Departments of Neurobiology, Biomedical Engineering, Psychology and Neuroscience, Neurology, Neurosurgery, and Duke University Center for Neuroengineering, Duke University, Durham, NC 27710, U.S.A.; and Edmund and Lily Safra International Institute of Neuroscience, Natal Brazil 59066060"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.01038.2011"},{"key":"B2","author":"Bahdanau D.","year":"2014","journal-title":"Neural machine translation by jointly learning to align and translate"},{"key":"B3","first-page":"1475","volume-title":"Advances in neural information processing systems","volume":"14","author":"Bakker B.","year":"2002"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0102-6"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.0000042"},{"key":"B7","first-page":"5930","author":"Chen Z.","year":"2013","journal-title":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/8\/4\/045005"},{"key":"B9","first-page":"577","volume":"28","author":"Chorowski J. 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