Open Access
| Issue |
ITM Web Conf.
Volume 30, 2019
29th International Crimean Conference “Microwave & Telecommunication Technology” (CriMiCo’2019)
|
|
|---|---|---|
| Article Number | 04011 | |
| Number of page(s) | 6 | |
| Section | Information Technology in Telecommunications (3a) | |
| DOI | https://doi.org/10.1051/itmconf/20193004011 | |
| Published online | 27 November 2019 | |
- W. Maass, Networks of spiking neurons: the third generation of neural network models, Neural Networks, v. 10(9), pp. 1659–1671 (1997) [CrossRef] [Google Scholar]
- F. Ponulak, A. Kasiński, Introduction to spiking neural networks: information processing,learning and applications, Acta Neurobiol., Exp. 71, pp. 409–433 (2011) [Google Scholar]
- A. Grüning, S.M. Bohte, Spiking neural networks: principles and challenges, Proc. of the 22nd European symposium on artificial neural networks, computational intelligence and machine learning (ESANN 2014), Bruges (Belgium), April 23-25, 2014, i6doc.com publ, (Louvain-La-Neuve, 2014) [Google Scholar]
- C. Kaldy, A.A. Lazar, E.K. Simonyi, L.Т. Toth, Time Encoded Communications for Human Area Network Biomonitoring, 8 p., https://pdfs.semanticscholar.org/2790/fee9608b9d48f98286304b67395a0d0fe406.pdf [Google Scholar]
- V. Bondarev, Vector-matrix models of pulse neuron for digital signal processing, Advances in Neural Networks—ISNN 2016, Lecture Notes in Computer Science, v. 9719, pp. 647–656 (2016) https://doi.org/10.1007/978-3-319-40663-3_74 [CrossRef] [Google Scholar]
- V. Bondarev, Pulse neuron learning rules for processing of dynamical variables encoded by pulse trains, Advances in Neural Computation, Machine Learning, and Cognitive Research, Studies in Computational Intelligence, v. 736, pp. 53–58 (2018) https://doi.org/10.1007/978-3-319-66604-4_8 [Google Scholar]
- V. Bondarev, Linear Filtering Based on a Pulsed Neuron Model with an Orthogonal Filter Bank, Advances in Neural Computation, Machine Learning, and Cognitive Research, Studies in Computational Intelligence, v. 799, pp. 101–108 (2019) https://doi.org/10.1007/978-3-030-01328-8_9 [Google Scholar]
- V.N. Bondarev, Tsifrovaya obrabotka signalov s ispol‘zovaniyem impul‘snykh neyronnykh setey (Digital signal processing with pulsed neural networks), Neuroinformatics 2018, XX International Scientific and Technical Conference: Lectures on Neuroinformatics (Мoscow, MIFI, pp. 44–71, 2018) (in Russian) [Google Scholar]
- A. Cichocki, S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications (Chichester, John Wiley & Sons, 2002) [Google Scholar]
- B.W. Gillespie, H.S. Malvar, D.A.F. Florêncio, Speech dereverberation Deconvolution via Maximum-Kurtosis Subband Adaptive Filtering, Proc ICASSP May 7–11 2001, v. 6, pp. 3701–3704 (Salt Lake City, UT, 2001) [Google Scholar]
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