{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:11:44Z","timestamp":1774721504299,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,3,17]]},"DOI":"10.1145\/3381755.3381758","type":"proceedings-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T23:09:51Z","timestamp":1592521791000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["Evolutionary Optimization for Neuromorphic Systems"],"prefix":"10.1145","author":[{"given":"Catherine D.","family":"Schuman","sequence":"first","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee"}]},{"given":"J. Parker","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee"}]},{"given":"Robert M.","family":"Patton","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee"}]},{"given":"Thomas E.","family":"Potok","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee"}]},{"given":"James S.","family":"Plank","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee"}]}],"member":"320","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"S.M. Bohte J.N. Kok and J.A. La Poutr\u00e9. 2000. SpikeProp: backpropagation for networks of spiking neurons.. In ESANN. 419--424.  S.M. Bohte J.N. Kok and J.A. La Poutr\u00e9. 2000. SpikeProp: backpropagation for networks of spiking neurons.. In ESANN. 419--424."},{"key":"e_1_3_2_1_2_1","unstructured":"G. Brockman V. Cheung L. Pettersson J. Schneider J. Schulman J. Tang and W. Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016).  G. Brockman V. Cheung L. Pettersson J. Schneider J. Schulman J. Tang and W. Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016)."},{"key":"e_1_3_2_1_3_1","volume-title":"IEEE International Conference on Rebooting Computing","author":"Buckley S.","unstructured":"S. Buckley , A. N. McCaughan , J. Chiles , R. P. Mirin , S. W. Nam , J. M. Shainline , G. Bruer , J. S. Plank , and C. D. Schuman . 2018. Design of superconducting optoelectronic networks for neuromorphic computing . In IEEE International Conference on Rebooting Computing . Tysons, VA, 36--42. S. Buckley, A. N. McCaughan, J. Chiles, R. P. Mirin, S. W. Nam, J. M. Shainline, G. Bruer, J. S. Plank, and C. D. Schuman. 2018. Design of superconducting optoelectronic networks for neuromorphic computing. In IEEE International Conference on Rebooting Computing. Tysons, VA, 36--42."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-011-9130-9"},{"key":"e_1_3_2_1_5_1","first-page":"379","article-title":"Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices. In Proceedings of ACM Great Lake Symposium on VLSI (GLSVLSI)","author":"Chakma G.","year":"2018","unstructured":"G. Chakma , N. D. Skuda , C. D. Schuman , J. S. Plank , M. E. Dean , and G. S. Rose . 2018 . Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices. In Proceedings of ACM Great Lake Symposium on VLSI (GLSVLSI) . Chicago , IL , 379 -- 383 . G. Chakma, N. D. Skuda, C. D. Schuman, J. S. Plank, M. E. Dean, and G. S. Rose. 2018. Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices. In Proceedings of ACM Great Lake Symposium on VLSI (GLSVLSI). Chicago, IL, 379--383.","journal-title":"Chicago"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2016.7738691"},{"key":"e_1_3_2_1_8_1","volume-title":"Potok","author":"Dimovska Mihaela","year":"2019","unstructured":"Mihaela Dimovska , J. Travis Johnston , Catherine D. Schuman , J. Parker Mitchell , and Thomas E . Potok . 2019 . Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks. In 2019 IEEE Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference. IEEE, In press . Mihaela Dimovska, J. Travis Johnston, Catherine D. Schuman, J. Parker Mitchell, and Thomas E. Potok. 2019. Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks. In 2019 IEEE Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference. IEEE, In press."},{"key":"e_1_3_2_1_9_1","volume-title":"DANNA: A Neuromorphic Software Ecosystem. Biologically Inspired Cognitive Architectures 9 (July","author":"Disney A.","year":"2016","unstructured":"A. Disney , J. Reynolds , C.D. Schuman , A. Klibisz , A. Young , and J. S. Plank . 2016 . DANNA: A Neuromorphic Software Ecosystem. Biologically Inspired Cognitive Architectures 9 (July 2016), 49--56. A.Disney, J. Reynolds, C.D. Schuman, A. Klibisz, A. Young, and J. S. Plank. 2016. DANNA: A Neuromorphic Software Ecosystem. Biologically Inspired Cognitive Architectures 9 (July 2016), 49--56."},{"key":"e_1_3_2_1_10_1","volume-title":"Four Simulators of the DANNA Neuromorphic Computing Architecture. In International Conference on Neuromorphic Computing Systems. ACM","author":"Disney A. W.","unstructured":"A. W. Disney , J. S. Plank , and M. Dean . 2018 . Four Simulators of the DANNA Neuromorphic Computing Architecture. In International Conference on Neuromorphic Computing Systems. ACM , Knoxville, TN. A. W. Disney, J. S. Plank, and M. Dean. 2018. Four Simulators of the DANNA Neuromorphic Computing Architecture. In International Conference on Neuromorphic Computing Systems. ACM, Knoxville, TN."},{"key":"e_1_3_2_1_11_1","volume-title":"Seung Hwan Lee, and Wei D Lu","author":"Du Chao","year":"2017","unstructured":"Chao Du , Fuxi Cai , Mohammed A Zidan , Wen Ma , Seung Hwan Lee, and Wei D Lu . 2017 . Reservoir computing using dynamic memristors for temporal information processing. Nature communications 8, 1 (2017), 2204. Chao Du, Fuxi Cai, Mohammed A Zidan, Wen Ma, Seung Hwan Lee, and Wei D Lu. 2017. Reservoir computing using dynamic memristors for temporal information processing. Nature communications 8, 1 (2017), 2204."},{"key":"e_1_3_2_1_12_1","unstructured":"Steve K Esser Rathinakumar Appuswamy Paul Merolla John V Arthur and Dharmendra S Modha. 2015. Backpropagation for energy-efficient neuromorphic computing. In Advances in Neural Information Processing Systems. 1117--1125.  Steve K Esser Rathinakumar Appuswamy Paul Merolla John V Arthur and Dharmendra S Modha. 2015. Backpropagation for energy-efficient neuromorphic computing. In Advances in Neural Information Processing Systems. 1117--1125."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"P. Ferr\u00e9 F. Mamalet and S.J. Thorpe. [n.d.]. Unsupervised feature learning with winner-takes-all based STDP. Frontiers in computational neuroscience 12 ([n. d.]).  P. Ferr\u00e9 F. Mamalet and S.J. Thorpe. [n.d.]. Unsupervised feature learning with winner-takes-all based STDP. Frontiers in computational neuroscience 12 ([n. d.]).","DOI":"10.3389\/fncom.2018.00024"},{"key":"e_1_3_2_1_14_1","volume-title":"Neuroevolution: from architectures to learning. Evolutionary intelligence 1, 1","author":"Floreano Dario","year":"2008","unstructured":"Dario Floreano , Peter D\u00fcrr , and Claudio Mattiussi . 2008. Neuroevolution: from architectures to learning. Evolutionary intelligence 1, 1 ( 2008 ), 47--62. Dario Floreano, Peter D\u00fcrr, and Claudio Mattiussi. 2008. Neuroevolution: from architectures to learning. Evolutionary intelligence 1, 1 (2008), 47--62."},{"key":"e_1_3_2_1_15_1","first-page":"937","article-title":"Accelerated neural evolution through cooperatively coevolved synapses","author":"Gomez F.","year":"2008","unstructured":"F. Gomez , J. Schmidhuber , and R. Miikkulainen . 2008 . Accelerated neural evolution through cooperatively coevolved synapses . Journal of Machine Learning Research 9 , May (2008), 937 -- 965 . F. Gomez, J. Schmidhuber, and R. Miikkulainen. 2008. Accelerated neural evolution through cooperatively coevolved synapses. Journal of Machine Learning Research 9, May (2008), 937--965.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/DCAS.2018.8620187"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.09.049"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"E. Kim J. Yarnall P. Shaha and G. T. Kenyon. 2019. A Neuromorphic Sparse Coding Defense to Adversarial Images. 8 pages.  E. Kim J. Yarnall P. Shaha and G. T. Kenyon. 2019. A Neuromorphic Sparse Coding Defense to Adversarial Images. 8 pages.","DOI":"10.1145\/3354265.3354277"},{"key":"e_1_3_2_1_19_1","volume-title":"Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Frontiers in neuroscience 9","author":"Kudithipudi Dhireesha","year":"2016","unstructured":"Dhireesha Kudithipudi , Qutaiba Saleh , Cory Merkel , James Thesing , and Bryant Wysocki . 2016. Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Frontiers in neuroscience 9 ( 2016 ), 502. Dhireesha Kudithipudi, Qutaiba Saleh, Cory Merkel, James Thesing, and Bryant Wysocki. 2016. Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Frontiers in neuroscience 9 (2016), 502."},{"key":"e_1_3_2_1_20_1","volume-title":"Training deep spiking neural networks using backpropagation. Frontiers in neuroscience 10","author":"Lee Jun Haeng","year":"2016","unstructured":"Jun Haeng Lee , Tobi Delbruck , and Michael Pfeiffer . 2016. Training deep spiking neural networks using backpropagation. Frontiers in neuroscience 10 ( 2016 ), 508. Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer. 2016. Training deep spiking neural networks using backpropagation. Frontiers in neuroscience 10 (2016), 508."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"R. Miikkulainen J. Liang E. Meyerson A. Rawal D. Fink O. Francon B. Raju H. Shahrzad A. Navruzyan N. Duffy etal 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Elsevier 293--312.  R. Miikkulainen J. Liang E. Meyerson A. Rawal D. Fink O. Francon B. Raju H. Shahrzad A. Navruzyan N. Duffy et al. 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Elsevier 293--312.","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"e_1_3_2_1_22_1","volume-title":"Potok","author":"Mitchell J. Parker","year":"2019","unstructured":"J. Parker Mitchell , Catherine D. Schuman , Robert M. Patton , and Thomas E . Potok . 2019 . Caspian : A Neuromorphic Development Platform. In Submitted . J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, and Thomas E. Potok. 2019. Caspian: A Neuromorphic Development Platform. In Submitted."},{"key":"e_1_3_2_1_23_1","volume-title":"Neuromorphic Systems. In 44th Annual GOMACTech Conference","author":"Plank J. S.","unstructured":"J. S. Plank , C. Rizzo , K. Shahat , G. Bruer , T. Dixon , M. Goin , G. Zhao , J. Anantharaj , C. D. Schuman , M. E. Dean , G. S. Rose , N. C. Cady , and J. Van Nostrand . 2019. The TENNLab Suite of LIDAR-Based Control Applications for Recurrent, Spiking , Neuromorphic Systems. In 44th Annual GOMACTech Conference . Albuquerque. J. S. Plank, C. Rizzo, K. Shahat, G. Bruer, T. Dixon, M. Goin, G. Zhao, J. Anantharaj, C. D. Schuman, M. E. Dean, G. S. Rose, N. C. Cady, and J. Van Nostrand. 2019. The TENNLab Suite of LIDAR-Based Control Applications for Recurrent, Spiking, Neuromorphic Systems. In 44th Annual GOMACTech Conference. Albuquerque."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/LOCS.2018.2885976"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2016.7738687"},{"key":"e_1_3_2_1_26_1","volume-title":"NengoDL: Combining deep learning and neuromorphic modelling methods. Neuroinformatics","author":"Rasmussen Daniel","year":"2019","unstructured":"Daniel Rasmussen . 2019. NengoDL: Combining deep learning and neuromorphic modelling methods. Neuroinformatics ( 2019 ), 1--18. Daniel Rasmussen. 2019. NengoDL: Combining deep learning and neuromorphic modelling methods. Neuroinformatics (2019), 1--18."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852472"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 15th european symposium on artificial neural networks. 471--482","author":"Schrauwen B.","unstructured":"B. Schrauwen , D. Verstraeten , and J. Van Campenhout . 2007. An overview of reservoir computing: theory, applications and implementations . In Proceedings of the 15th european symposium on artificial neural networks. 471--482 . B. Schrauwen, D. Verstraeten, and J. Van Campenhout. 2007. An overview of reservoir computing: theory, applications and implementations. In Proceedings of the 15th european symposium on artificial neural networks. 471--482."},{"key":"e_1_3_2_1_29_1","volume-title":"Understanding Selection And Diversity For Evolution Of Spiking Recurrent Neural Networks. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.","author":"Schuman C.D.","unstructured":"C.D. Schuman , G. Bruer , A.R. Young , M. Dean , and J.S. Plank . 2018 . Understanding Selection And Diversity For Evolution Of Spiking Recurrent Neural Networks. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8. C.D. Schuman, G. Bruer, A.R. Young, M. Dean, and J.S. Plank. 2018. Understanding Selection And Diversity For Evolution Of Spiking Recurrent Neural Networks. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8."},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the Workshop on Machine Learning in High Performance Computing Environments. IEEE Press, 36--46","author":"Schuman C.D.","unstructured":"C.D. Schuman , A. Disney , S.P. Singh , G. Bruer , J.P. Mitchell , A. Klibisz , and J.S. Plank . 2016. Parallel evolutionary optimization for neuromorphic network training . In Proceedings of the Workshop on Machine Learning in High Performance Computing Environments. IEEE Press, 36--46 . C.D. Schuman, A. Disney, S.P. Singh, G. Bruer, J.P. Mitchell, A. Klibisz, and J.S. Plank. 2016. Parallel evolutionary optimization for neuromorphic network training. In Proceedings of the Workshop on Machine Learning in High Performance Computing Environments. IEEE Press, 36--46."},{"key":"e_1_3_2_1_31_1","volume-title":"2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 145--154","author":"Schuman C.D.","unstructured":"C.D. Schuman , J.S. Plank , A. Disney , and J. Reynolds . 2016. An evolutionary optimization framework for neural networks and neuromorphic architectures . In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 145--154 . C.D. Schuman, J.S. Plank, A. Disney, and J. Reynolds. 2016. An evolutionary optimization framework for neural networks and neuromorphic architectures. In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 145--154."},{"key":"e_1_3_2_1_32_1","volume-title":"Neuromorphic Computing Symposium (NCS '17)","author":"Schuman C. D.","year":"1835","unstructured":"C. D. Schuman , T. E. Potok , S. Young , R. Patton , G. Perdue , G. Chakma , A. Wyer , and G. S. Rose . 2017. Neuromorphic computing for temporal scientific data classification . In Neuromorphic Computing Symposium (NCS '17) . ACM, New York, NY, USA, Article 2, 6 pages. https:\/\/doi.org\/10.1145\/3 1835 84.3183612 10.1145\/3183584.3183612 C. D. Schuman, T. E. Potok, S. Young, R. Patton, G. Perdue, G. Chakma, A. Wyer, and G. S. Rose. 2017. Neuromorphic computing for temporal scientific data classification. In Neuromorphic Computing Symposium (NCS '17). ACM, New York, NY, USA, Article 2, 6 pages. https:\/\/doi.org\/10.1145\/3183584.3183612"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-018-0015-y"},{"key":"e_1_3_2_1_34_1","volume-title":"SLAYER: Spike layer error reassignment in time. In Advances in Neural Information Processing Systems. 1412--1421.","author":"Shrestha S.B.","year":"2018","unstructured":"S.B. Shrestha and G. Orchard . 2018 . SLAYER: Spike layer error reassignment in time. In Advances in Neural Information Processing Systems. 1412--1421. S.B. Shrestha and G. Orchard. 2018. SLAYER: Spike layer error reassignment in time. In Advances in Neural Information Processing Systems. 1412--1421."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"K.O. Stanley and R. Miikkulainen. 2002. Evolving neural networks through augmenting topologies. Evolutionary computation 10 2 (2002) 99--127.  K.O. Stanley and R. Miikkulainen. 2002. Evolving neural networks through augmenting topologies. Evolutionary computation 10 2 (2002) 99--127.","DOI":"10.1162\/106365602320169811"},{"key":"e_1_3_2_1_36_1","volume-title":"timescale invariant unsupervised online deep learning with STDP. Frontiers in computational neuroscience 12","author":"Thiele Johannes C","year":"2018","unstructured":"Johannes C Thiele , Olivier Bichler , and Antoine Dupret . 2018. Event-based , timescale invariant unsupervised online deep learning with STDP. Frontiers in computational neuroscience 12 ( 2018 ), 46. Johannes C Thiele, Olivier Bichler, and Antoine Dupret. 2018. Event-based, timescale invariant unsupervised online deep learning with STDP. Frontiers in computational neuroscience 12 (2018), 46."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.784219"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2834892.2834896"}],"event":{"name":"NICE '20: Neuro-inspired Computational Elements Workshop","location":"Heidelberg Germany","acronym":"NICE '20","sponsor":["INTEL Intel Corporation","IBM IBM"]},"container-title":["Proceedings of the Neuro-inspired Computational Elements Workshop"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3381755.3381758","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3381755.3381758","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:07Z","timestamp":1750199587000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3381755.3381758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,17]]},"references-count":38,"alternative-id":["10.1145\/3381755.3381758","10.1145\/3381755"],"URL":"https:\/\/doi.org\/10.1145\/3381755.3381758","relation":{},"subject":[],"published":{"date-parts":[[2020,3,17]]},"assertion":[{"value":"2020-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}