{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:27:35Z","timestamp":1764937655720,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["23-11-00260"],"award-info":[{"award-number":["23-11-00260"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In this paper, we demonstrate that fixed-weight layers generated from random distribution or logistic functions can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher\u2019s Iris, Wisconsin Breast Cancer, and MNIST datasets. We have observed that logistic functions yield high accuracy with less dispersion in results. We have also assessed the precision of our approach under conditions of minimizing the number of spikes generated in the network. It is practically useful for reducing energy consumption in spiking neural networks. Our findings reveal that the proposed method demonstrates the highest accuracy on Fisher\u2019s iris and MNIST datasets with decoding using logistic regression. Furthermore, they surpass the accuracy of the conventional (non-spiking) approach using only logistic regression in the case of Wisconsin Breast Cancer. We have also investigated the impact of non-stochastic spike generation on accuracy.<\/jats:p>","DOI":"10.3390\/bdcc7040184","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T05:41:35Z","timestamp":1702878095000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Efficient Spiking Neural Network Classifier"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-4133","authenticated-orcid":false,"given":"Alexander","family":"Sboev","sequence":"first","affiliation":[{"name":"Kurchatov\u2019s Complex of NBICS-Technologies, National Research Center \u201cKurchatov Institute\u201d, Academic Kurchatov sq., 123182 Moscow, Russia"},{"name":"Institute for Laser and Plasma Technologies, National Research Nuclear University \u201cMEPhI\u201d, 115409 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5595-6398","authenticated-orcid":false,"given":"Roman","family":"Rybka","sequence":"additional","affiliation":[{"name":"Kurchatov\u2019s Complex of NBICS-Technologies, National Research Center \u201cKurchatov Institute\u201d, Academic Kurchatov sq., 123182 Moscow, Russia"},{"name":"Department of Automated Systems of Organizational Management, Russian Technological University \u201cMIREA\u201d, Vernadsky av., 119296 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9640-6787","authenticated-orcid":false,"given":"Dmitry","family":"Kunitsyn","sequence":"additional","affiliation":[{"name":"Kurchatov\u2019s Complex of NBICS-Technologies, National Research Center \u201cKurchatov Institute\u201d, Academic Kurchatov sq., 123182 Moscow, Russia"},{"name":"Institute for Laser and Plasma Technologies, National Research Nuclear University \u201cMEPhI\u201d, 115409 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-9879","authenticated-orcid":false,"given":"Alexey","family":"Serenko","sequence":"additional","affiliation":[{"name":"Kurchatov\u2019s Complex of NBICS-Technologies, National Research Center \u201cKurchatov Institute\u201d, Academic Kurchatov sq., 123182 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3243-3436","authenticated-orcid":false,"given":"Vyacheslav","family":"Ilyin","sequence":"additional","affiliation":[{"name":"Kurchatov\u2019s Complex of NBICS-Technologies, National Research Center \u201cKurchatov Institute\u201d, Academic Kurchatov sq., 123182 Moscow, Russia"},{"name":"National Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, Russia"},{"name":"Department of NBIC-Technologies, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5707-7760","authenticated-orcid":false,"given":"Vadim","family":"Putrolaynen","sequence":"additional","affiliation":[{"name":"Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Diehl, P.U., Pedroni, B.U., Cassidy, A., Merolla, P., Neftci, E., and Zarrella, G. (2016, January 24\u201329). TrueHappiness: Neuromorphic emotion recognition on TrueNorth. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727758"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MM.2018.112130359","article-title":"Loihi: A neuromorphic manycore processor with on-chip learning","volume":"38","author":"Davies","year":"2018","journal-title":"IEEE Micro"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41586-019-1424-8","article-title":"Towards artificial general intelligence with hybrid Tianjic chip architecture","volume":"572","author":"Pei","year":"2019","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1038\/s41586-022-04992-8","article-title":"A compute-in-memory chip based on resistive random-access memory","volume":"608","author":"Wan","year":"2022","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"153876","DOI":"10.1016\/j.apsusc.2022.153876","article-title":"Implementation of reservoir computing using volatile WOx-based memristor","volume":"599","author":"Kim","year":"2022","journal-title":"Appl. Surf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"33244","DOI":"10.1021\/acsami.1c06618","article-title":"Tunable synaptic characteristics of a Ti\/TiO2\/Si memory device for reservoir computing","volume":"13","author":"Yang","year":"2021","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103502","DOI":"10.1063\/5.0036667","article-title":"Nitrogen-induced ultralow power switching in flexible ZnO-based memristor for artificial synaptic learning","volume":"118","author":"Lin","year":"2021","journal-title":"Appl. Phys. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neucom.2012.12.032","article-title":"A parallel algorithm for skeletonizing images by using spiking neural P systems","volume":"115","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111223","DOI":"10.1016\/j.chaos.2021.111223","article-title":"Implementation of a reservoir computing system using the short-term effects of Pt\/HfO2\/TaOx\/TiN memristors with self-rectification","volume":"150","author":"Ryu","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"358","DOI":"10.3389\/fnins.2020.00358","article-title":"Neurohybrid memristive CMOS-integrated systems for biosensors and neuroprosthetics","volume":"14","author":"Mikhaylov","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Q., Pan, G., and Jiang, Y. (2022). An Ultra-Low Power Threshold Voltage Variable Artificial Retina Neuron. Electronics, 11.","DOI":"10.3390\/electronics11030365"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"150563","DOI":"10.1016\/j.apsusc.2021.150563","article-title":"Short-term and long-term synaptic plasticity in Ag\/HfO2\/SiO2\/Si stack by controlling conducting filament strength","volume":"565","author":"Lee","year":"2021","journal-title":"Appl. Surf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2100151","DOI":"10.1002\/aisy.202100151","article-title":"Spike-Enabled Audio Learning in Multilevel Synaptic Memristor Array-Based Spiking Neural Network","volume":"4","author":"Wu","year":"2022","journal-title":"Adv. Intell. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1109\/TNNLS.2020.3044364","article-title":"The heidelberg spiking data sets for the systematic evaluation of spiking neural networks","volume":"33","author":"Cramer","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_15","unstructured":"Tang, G., Kumar, N., Yoo, R., and Michmizos, K. (2020, January 16\u201318). Deep reinforcement learning with population-coded spiking neural network for continuous control. Proceedings of the 2020 Conference on Robot Learning, Virtual Event\/Cambridge, MA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Matsukatova, A.N., Iliasov, A.I., Nikiruy, K.E., Kukueva, E.V., Vasiliev, A.L., Goncharov, B.V., Sitnikov, A.V., Zanaveskin, M.L., Bugaev, A.S., and Demin, V.A. (2022). Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B) x (LiNbO3)100-x Nanocomposite Memristors. Nanomaterials, 12.","DOI":"10.3390\/nano12193455"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/TMSCS.2017.2761231","article-title":"Parameter exploration to improve performance of memristor-based neuromorphic architectures","volume":"4","author":"Shahsavari","year":"2017","journal-title":"IEEE Trans.-Multi-Scale Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","article-title":"Reservoir computing approaches to recurrent neural network training","volume":"3","author":"Jaeger","year":"2009","journal-title":"Comput. Sci. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Velichko, A. (2020). Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map. Electronics, 9.","DOI":"10.3390\/electronics9091432"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sboev, A.G., Serenko, A.V., Kunitsyn, D.E., Rybka, R.B., and Putrolaynen, V.V. (2023, January 23\u201327). Towards Solving Classification Tasks Using Spiking Neurons with Fixed Weights. Proceedings of the International Conference on Neuroinformatics, Moscow, Russia.","DOI":"10.1007\/978-3-031-44865-2_11"},{"key":"ref_21","unstructured":"Orhan, E. (2023, December 13). The Leaky Integrate-and-Fire Neuron Model. Available online: http:\/\/www.cns.nyu.edu\/~eorhan\/notes\/lif-neuron.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7802","DOI":"10.1002\/mma.6241","article-title":"Solving a classification task by spiking neural network with STDP based on rate and temporal input encoding","volume":"43","author":"Sboev","year":"2020","journal-title":"Math. Methods Appl. Sci."},{"key":"ref_24","first-page":"1","article-title":"Computing with spiking neuron networks","volume":"1","author":"Bohte","year":"2012","journal-title":"Handb. Nat. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sboev, A., Vlasov, D., Rybka, R., Davydov, Y., Serenko, A., and Demin, V. (2021). Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks. Mathematics, 9.","DOI":"10.3390\/math9243237"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TCDS.2021.3049487","article-title":"In Situ Learning in Hardware Compatible Multilayer Memristive Spiking Neural Network","volume":"14","author":"Li","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126773","DOI":"10.1016\/j.neucom.2023.126773","article-title":"Desire backpropagation: A lightweight training algorithm for multi-layer spiking neural networks based on spike-timing-dependent plasticity","volume":"560","author":"Gerlinghoff","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neunet.2020.11.005","article-title":"Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network","volume":"134","author":"Demin","year":"2021","journal-title":"Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.orgel.2019.06.052","article-title":"On the resistive switching mechanism of parylene-based memristive devices","volume":"74","author":"Minnekhanov","year":"2019","journal-title":"Org. Electron."},{"key":"ref_30","unstructured":"Sboev, A., Kunitsyn, D., and Balykov, M.A. (2023, January 13\u201315). Spoken Digits Classification Using a Spiking Neural Network with Fixed Synaptic Weights. Proceedings of the 2023 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, the 14th Annual Meeting of the BICA Society, Ningbo, China. in press."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/4\/184\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:46Z","timestamp":1760132446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/4\/184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,18]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["bdcc7040184"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7040184","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2023,12,18]]}}}