Abstract
Anomaly detection is an important problem in various fields of complex systems research including image processing, data analysis, physical security and cybersecurity. In image processing, it is used for removing noise while preserving image quality, and in data analysis, physical security and cybersecurity, it is used to find interesting data points, objects or events in a vast sea of information. Anomaly detection will continue to be an important problem in domains intersecting with “Big Data”. In this paper we provide a novel algorithm for anomaly detection that uses phase-coded spiking neurons as basic computational elements.
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Acknowledgments
This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U. S. Department of Energy’s National Nuclear Security Administration under Contract DE-NA0003525. SAND No. 2018-5891 C.
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Verzi, S.J., Vineyard, C.M., Aimone, J.B. (2018). Neural-Inspired Anomaly Detection. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_21
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DOI: https://doi.org/10.1007/978-3-319-96661-8_21
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