{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:33:04Z","timestamp":1750221184832,"version":"3.41.0"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100016818","name":"UT-Battelle, LLC","doi-asserted-by":"crossref","award":["DE-AC0500OR22725"],"award-info":[{"award-number":["DE-AC0500OR22725"]}],"id":[{"id":"10.13039\/100016818","id-type":"DOI","asserted-by":"crossref"}]},{"name":"DOE Public Access Plan"},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"name":"United States Department of Defense and used resources of the Computational Research and Development Programs at Oak Ridge National Laboratory"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2018,10,31]]},"abstract":"<jats:p>We study the applicability of spiking neural networks and neuromorphic hardware for solving general opti- mization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems to construct a fully connected spiking neural system to generate synchronous spike responses indicative of the underlying community structure in an undirected, unweighted graph. Mapping this fully connected system to current generation neuromorphic hardware is done by embedding sparse tree graphs to generate only the leading-order spiking dynamics. We demonstrate that for a chosen set of benchmark graphs, the spike responses generated on a current generation neuromorphic processor can improve the stability of graph partitions and non-overlapping communities can be identified even with the loss of higher-order spiking behavior if the graphs are sufficiently dense. For sparse graphs, the loss of higher-order spiking behavior improves the stability of certain graph partitions but does not retrieve the known community memberships.<\/jats:p>","DOI":"10.1145\/3223048","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T13:18:59Z","timestamp":1543324739000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection"],"prefix":"10.1145","volume":"14","author":[{"given":"Kathleen E.","family":"Hamilton","sequence":"first","affiliation":[{"name":"Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neena","family":"Imam","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Travis S.","family":"Humble","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6707077"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2015.7169244"},{"key":"e_1_2_1_3_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_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1604850113"},{"volume-title":"Brian: A simulator for spiking neural networks in python. Front. Neuroinform. 2","year":"2008","author":"Goodman Dan","key":"e_1_2_1_5_1"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183584.3183621"},{"key":"e_1_2_1_7_1","unstructured":"Kathleen E. Hamilton and Travis S. Humble. 2018. Spiking spin-glass models for label propagation and community detection. arXiv Preprint arXiv:1801.03571 (2018).  Kathleen E. Hamilton and Travis S. Humble. 2018. Spiking spin-glass models for label propagation and community detection. arXiv Preprint arXiv:1801.03571 (2018)."},{"volume":"1","volume-title":"Santa Fe Institute Studies in the Sciences of Complexity","author":"Hertz John","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.79.8.2554"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/BF00339943","article-title":"Neural computation of decisions in optimization problems","volume":"52","author":"Hopfield John J.","year":"1985","journal-title":"Biol. Cybernet."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.2853-10.2011"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1372754"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2006.11.013"},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Paul A. Merolla John V. Arthur Rodrigo Alvarez-Icaza Andrew S. Cassidy Jun Sawada Filipp Akopyan Bryan L. Jackson Nabil Imam Chen Guo Yutaka Nakamura etal 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345 6197 (2014) 668--673.  Paul A. Merolla John V. Arthur Rodrigo Alvarez-Icaza Andrew S. Cassidy Jun Sawada Filipp Akopyan Bryan L. Jackson Nabil Imam Chen Guo Yutaka Nakamura et al. 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. 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