{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T14:04:34Z","timestamp":1750255474362,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"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":[[2021,7,27]]},"DOI":"10.1145\/3477145.3477155","type":"proceedings-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T14:38:20Z","timestamp":1634135900000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Neko: a Library for Exploring Neuromorphic Learning Rules"],"prefix":"10.1145","author":[{"given":"Zixuan","family":"Zhao","sequence":"first","affiliation":[{"name":"The University of Chicago"}]},{"given":"Nathan","family":"Wycoff","sequence":"additional","affiliation":[{"name":"Virginia Tech"}]},{"given":"Neil","family":"Getty","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}]},{"given":"Rick","family":"Stevens","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}]},{"given":"Fangfang","family":"Xia","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}]}],"member":"320","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265\u2013283.","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265\u2013283. Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265\u2013283."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-021-00809-5"},{"key":"e_1_3_2_1_3_1","unstructured":"Mohamed Akrout Collin Wilson Peter\u00a0C Humphreys Timothy Lillicrap and Douglas Tweed. 2019. Deep learning without weight transport. arXiv preprint arXiv:1904.05391(2019).  Mohamed Akrout Collin Wilson Peter\u00a0C Humphreys Timothy Lillicrap and Douglas Tweed. 2019. Deep learning without weight transport. arXiv preprint arXiv:1904.05391(2019)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/0957-4484\/23\/7\/075201"},{"key":"e_1_3_2_1_5_1","volume-title":"A tutorial on adaptive MCMC. Statistics and Computing 18, 4 (01","author":"Andrieu Christophe","year":"2008","unstructured":"Christophe Andrieu and Johannes Thoms . 2008. A tutorial on adaptive MCMC. Statistics and Computing 18, 4 (01 Dec 2008 ), 343\u2013373. https:\/\/doi.org\/10.1007\/s11222-008-9110-y Christophe Andrieu and Johannes Thoms. 2008. A tutorial on adaptive MCMC. Statistics and Computing 18, 4 (01 Dec 2008), 343\u2013373. https:\/\/doi.org\/10.1007\/s11222-008-9110-y"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2013.00048"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17236-y"},{"volume-title":"The NEURON book","author":"Carnevale T","key":"e_1_3_2_1_8_1","unstructured":"Nicholas\u00a0 T Carnevale and Michael\u00a0 L Hines . 2006. The NEURON book . Cambridge University Press . Nicholas\u00a0T Carnevale and Michael\u00a0L Hines. 2006. The NEURON book. Cambridge University Press."},{"key":"e_1_3_2_1_9_1","unstructured":"Fran\u00e7ois Chollet 2015. Keras. https:\/\/keras.io.  Fran\u00e7ois Chollet 2015. Keras. https:\/\/keras.io."},{"key":"e_1_3_2_1_10_1","volume-title":"A distributional code for value in dopamine-based reinforcement learning. Nature 577, 7792","author":"Dabney Will","year":"2020","unstructured":"Will Dabney , Zeb Kurth-Nelson , Naoshige Uchida , Clara\u00a0Kwon Starkweather , Demis Hassabis , R\u00e9mi Munos , and Matthew Botvinick . 2020. A distributional code for value in dopamine-based reinforcement learning. Nature 577, 7792 ( 2020 ), 671\u2013675. Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara\u00a0Kwon Starkweather, Demis Hassabis, R\u00e9mi Munos, and Matthew Botvinick. 2020. A distributional code for value in dopamine-based reinforcement learning. Nature 577, 7792 (2020), 671\u2013675."},{"key":"e_1_3_2_1_11_1","first-page":"11","article-title":"PyNN: a common interface for neuronal network simulators","volume":"2","author":"Davison P","year":"2009","unstructured":"Andrew\u00a0 P Davison , Daniel Br\u00fcderle , Jochen\u00a0 M Eppler , Jens Kremkow , Eilif Muller , Dejan Pecevski , Laurent Perrinet , and Pierre Yger . 2009 . PyNN: a common interface for neuronal network simulators . Frontiers in Neuroinformatics 2 (2009), 11 . Andrew\u00a0P Davison, Daniel Br\u00fcderle, Jochen\u00a0M Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent Perrinet, and Pierre Yger. 2009. PyNN: a common interface for neuronal network simulators. Frontiers in Neuroinformatics 2 (2009), 11.","journal-title":"Frontiers in Neuroinformatics"},{"key":"e_1_3_2_1_12_1","unstructured":"Julien Dupeyroux. 2021. A toolbox for neuromorphic sensing in robotics. arxiv:2103.02751\u00a0[cs.RO]  Julien Dupeyroux. 2021. A toolbox for neuromorphic sensing in robotics. arxiv:2103.02751\u00a0[cs.RO]"},{"key":"e_1_3_2_1_13_1","volume-title":"Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 6440","author":"Fuller J","year":"2019","unstructured":"Elliot\u00a0 J Fuller , Scott\u00a0 T Keene , Armantas Melianas , Zhongrui Wang , Sapan Agarwal , Yiyang Li , Yaakov Tuchman , Conrad\u00a0 D James , Matthew\u00a0 J Marinella , J\u00a0Joshua Yang , 2019. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 6440 ( 2019 ), 570\u2013574. Elliot\u00a0J Fuller, Scott\u00a0T Keene, Armantas Melianas, Zhongrui Wang, Sapan Agarwal, Yiyang Li, Yaakov Tuchman, Conrad\u00a0D James, Matthew\u00a0J Marinella, J\u00a0Joshua Yang, 2019. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 6440 (2019), 570\u2013574."},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of The 33rd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a048)","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning . In Proceedings of The 33rd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a048) , Maria\u00a0Florina Balcan and Kilian\u00a0Q. Weinberger (Eds.). PMLR, New York, New York, USA, 1050\u20131059. http:\/\/proceedings.mlr.press\/v48\/gal16.html Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In Proceedings of The 33rd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a048), Maria\u00a0Florina Balcan and Kilian\u00a0Q. Weinberger (Eds.). PMLR, New York, New York, USA, 1050\u20131059. http:\/\/proceedings.mlr.press\/v48\/gal16.html"},{"key":"e_1_3_2_1_15_1","unstructured":"J. Garofolo Lori Lamel W. Fisher Jonathan Fiscus D. Pallett N. Dahlgren and V. Zue. 1992. TIMIT Acoustic-phonetic Continuous Speech Corpus. Linguistic Data Consortium (11 1992).  J. Garofolo Lori Lamel W. Fisher Jonathan Fiscus D. Pallett N. Dahlgren and V. Zue. 1992. TIMIT Acoustic-phonetic Continuous Speech Corpus. Linguistic Data Consortium (11 1992)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1430"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2986459.2986721"},{"key":"e_1_3_2_1_18_1","unstructured":"Sam Greydanus. 2020. Scaling down Deep Learning. arxiv:2011.14439\u00a0[cs.LG]  Sam Greydanus. 2020. Scaling down Deep Learning. arxiv:2011.14439\u00a0[cs.LG]"},{"key":"e_1_3_2_1_19_1","volume-title":"A First Course in Bayesian Statistical Methods","author":"Hoff D.","unstructured":"Peter\u00a0 D. Hoff . 2009. A First Course in Bayesian Statistical Methods ( 1 st ed.). Springer Publishing Company, Inc orporated. Peter\u00a0D. Hoff. 2009. A First Course in Bayesian Statistical Methods (1st ed.). Springer Publishing Company, Incorporated.","edition":"1"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2296777"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2018.00092"},{"key":"e_1_3_2_1_22_1","volume-title":"Knill and Alexandre Pouget","author":"C.","year":"2004","unstructured":"David\u00a0 C. Knill and Alexandre Pouget . 2004 . The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences 27, 12 (01 Dec 2004), 712\u2013719. https:\/\/doi.org\/10.1016\/j.tins.2004.10.007 David\u00a0C. Knill and Alexandre Pouget. 2004. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences 27, 12 (01 Dec 2004), 712\u2013719. https:\/\/doi.org\/10.1016\/j.tins.2004.10.007"},{"key":"e_1_3_2_1_23_1","unstructured":"Yann LeCun. 1998. The MNIST database of handwritten digits. http:\/\/yann. lecun. com\/exdb\/mnist\/(1998).  Yann LeCun. 1998. The MNIST database of handwritten digits. http:\/\/yann. lecun. com\/exdb\/mnist\/(1998)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00508"},{"key":"e_1_3_2_1_25_1","volume-title":"Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature communications 9, 1","author":"Li Can","year":"2018","unstructured":"Can Li , Daniel Belkin , Yunning Li , Peng Yan , Miao Hu , Ning Ge , Hao Jiang , Eric Montgomery , Peng Lin , Zhongrui Wang , 2018. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature communications 9, 1 ( 2018 ), 1\u20138. Can Li, Daniel Belkin, Yunning Li, Peng Yan, Miao Hu, Ning Ge, Hao Jiang, Eric Montgomery, Peng Lin, Zhongrui Wang, 2018. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature communications 9, 1 (2018), 1\u20138."},{"key":"e_1_3_2_1_26_1","volume-title":"Random synaptic feedback weights support error backpropagation for deep learning. Nature communications 7, 1","author":"Lillicrap P","year":"2016","unstructured":"Timothy\u00a0 P Lillicrap , Daniel Cownden , Douglas\u00a0 B Tweed , and Colin\u00a0 J Akerman . 2016. Random synaptic feedback weights support error backpropagation for deep learning. Nature communications 7, 1 ( 2016 ), 1\u201310. Timothy\u00a0P Lillicrap, Daniel Cownden, Douglas\u00a0B Tweed, and Colin\u00a0J Akerman. 2016. Random synaptic feedback weights support error backpropagation for deep learning. Nature communications 7, 1 (2016), 1\u201310."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41583-020-0277-3"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2018.157113521"},{"key":"e_1_3_2_1_29_1","first-page":"1","article-title":"A unified framework of online learning algorithms for training recurrent neural networks","volume":"21","author":"Marschall Owen","year":"2020","unstructured":"Owen Marschall , Kyunghyun Cho , and Cristina Savin . 2020 . A unified framework of online learning algorithms for training recurrent neural networks . Journal of Machine Learning Research 21 , 135 (2020), 1 \u2013 34 . Owen Marschall, Kyunghyun Cho, and Cristina Savin. 2020. A unified framework of online learning algorithms for training recurrent neural networks. Journal of Machine Learning Research 21, 135 (2020), 1\u201334.","journal-title":"Journal of Machine Learning Research"},{"volume-title":"MCMC Using Hamiltonian Dynamics","author":"Neal M.","key":"e_1_3_2_1_30_1","unstructured":"Radford\u00a0 M. Neal . 2011. MCMC Using Hamiltonian Dynamics . CRC Press . https:\/\/doi.org\/10.1201\/b10905-7 Radford\u00a0M. Neal. 2011. MCMC Using Hamiltonian Dynamics. CRC Press. https:\/\/doi.org\/10.1201\/b10905-7"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2931595"},{"key":"e_1_3_2_1_32_1","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga [n.d.]. PyTorch: An imperative style high-performance deep learning library. arXiv preprint arXiv:1912.01703([n.\u00a0d.]).  Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga [n.d.]. PyTorch: An imperative style high-performance deep learning library. arXiv preprint arXiv:1912.01703([n.\u00a0d.])."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2906158"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.436415"},{"key":"e_1_3_2_1_35_1","unstructured":"Bodo Rueckauer Connor Bybee Ralf Goettsche Yashwardhan Singh Joyesh Mishra and Andreas Wild. 2021. NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi. arXiv preprint arXiv:2101.04261(2021).  Bodo Rueckauer Connor Bybee Ralf Goettsche Yashwardhan Singh Joyesh Mishra and Andreas Wild. 2021. NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi. arXiv preprint arXiv:2101.04261(2021)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2018.8351295"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00682"},{"key":"e_1_3_2_1_38_1","unstructured":"Jo\u00e3o Sacramento Rui\u00a0Ponte Costa Yoshua Bengio and Walter Senn. 2018. Dendritic cortical microcircuits approximate the backpropagation algorithm. arXiv preprint arXiv:1810.11393(2018).  Jo\u00e3o Sacramento Rui\u00a0Ponte Costa Yoshua Bengio and Walter Senn. 2018. Dendritic cortical microcircuits approximate the backpropagation algorithm. arXiv preprint arXiv:1810.11393(2018)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2016.11"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3320288.3320305"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Marcel Stimberg Romain Brette and Dan\u00a0FM Goodman. 2019. Brian 2 an intuitive and efficient neural simulator. eLife 8(2019) e47314.  Marcel Stimberg Romain Brette and Dan\u00a0FM Goodman. 2019. Brian 2 an intuitive and efficient neural simulator. eLife 8(2019) e47314.","DOI":"10.7554\/eLife.47314"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1088\/0022-3727\/46\/9\/093001"},{"key":"e_1_3_2_1_43_1","volume-title":"Fully hardware-implemented memristor convolutional neural network. Nature 577, 7792","author":"Yao Peng","year":"2020","unstructured":"Peng Yao , Huaqiang Wu , Bin Gao , Jianshi Tang , Qingtian Zhang , Wenqiang Zhang , J\u00a0Joshua Yang , and He Qian . 2020. Fully hardware-implemented memristor convolutional neural network. Nature 577, 7792 ( 2020 ), 641\u2013646. Peng Yao, Huaqiang Wu, Bin Gao, Jianshi Tang, Qingtian Zhang, Wenqiang Zhang, J\u00a0Joshua Yang, and He Qian. 2020. Fully hardware-implemented memristor convolutional neural network. Nature 577, 7792 (2020), 641\u2013646."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/WISP.2015.7139171"},{"key":"e_1_3_2_1_45_1","volume-title":"Superspike: Supervised learning in multilayer spiking neural networks. Neural computation 30, 6","author":"Zenke Friedemann","year":"2018","unstructured":"Friedemann Zenke and Surya Ganguli . 2018 . Superspike: Supervised learning in multilayer spiking neural networks. Neural computation 30, 6 (2018), 1514\u20131541. Friedemann Zenke and Surya Ganguli. 2018. Superspike: Supervised learning in multilayer spiking neural networks. Neural computation 30, 6 (2018), 1514\u20131541."}],"event":{"name":"ICONS 2021: International Conference on Neuromorphic Systems 2021","acronym":"ICONS 2021","location":"Knoxville TN USA"},"container-title":["International Conference on Neuromorphic Systems 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477145.3477155","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477145.3477155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:03Z","timestamp":1750193343000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477145.3477155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,27]]},"references-count":45,"alternative-id":["10.1145\/3477145.3477155","10.1145\/3477145"],"URL":"https:\/\/doi.org\/10.1145\/3477145.3477155","relation":{},"subject":[],"published":{"date-parts":[[2021,7,27]]},"assertion":[{"value":"2021-10-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}