{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:21:36Z","timestamp":1776982896663,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"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":["21-11-00328"],"award-info":[{"award-number":["21-11-00328"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local learning and limited numbers of connections, respectively. In this work, we compare two methods of connectivity reduction that are applicable to spiking networks with local plasticity; instead of a large fully-connected network (which is used as the baseline for comparison), we employ either an ensemble of independent small networks or a network with probabilistic sparse connectivity. We evaluate both of these methods with a three-layer spiking neural network, which are applied to handwritten and spoken digit classification tasks using two memristive plasticity models and the classical spike time-dependent plasticity (STDP) rule. Both methods achieve an F1-score of 0.93\u20130.95 on the handwritten digits recognition task and 0.85\u20130.93 on the spoken digits recognition task. Applying a combination of both methods made it possible to obtain highly accurate models while reducing the number of connections by more than three times compared to the basic model.<\/jats:p>","DOI":"10.3390\/bdcc8030022","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T06:07:39Z","timestamp":1708668459000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparison of Bagging and Sparcity Methods for Connectivity Reduction in Spiking Neural Networks with Memristive Plasticity"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5595-6398","authenticated-orcid":false,"given":"Roman","family":"Rybka","sequence":"first","affiliation":[{"name":"National Research Centre \u201cKurchatov Institute\u201d, 123182 Moscow, Russia"},{"name":"Department of Automated Systems of Organizational Management, Russian Technological University \u201cMIREA\u201d, Vernadsky Av., 119296 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\/0000-0002-8596-4196","authenticated-orcid":false,"given":"Yury","family":"Davydov","sequence":"additional","affiliation":[{"name":"National Research Centre \u201cKurchatov Institute\u201d, 123182 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8314-3941","authenticated-orcid":false,"given":"Danila","family":"Vlasov","sequence":"additional","affiliation":[{"name":"National Research Centre \u201cKurchatov Institute\u201d, 123182 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":"National Research Centre \u201cKurchatov Institute\u201d, 123182 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-4133","authenticated-orcid":false,"given":"Alexander","family":"Sboev","sequence":"additional","affiliation":[{"name":"National Research Centre \u201cKurchatov Institute\u201d, 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\/0000-0003-3243-3436","authenticated-orcid":false,"given":"Vyacheslav","family":"Ilyin","sequence":"additional","affiliation":[{"name":"National Research Centre \u201cKurchatov Institute\u201d, 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"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1126\/science.1254642","article-title":"A million spiking-neuron integrated circuit with a scalable communication network and interface","volume":"345","author":"Merolla","year":"2014","journal-title":"Science"},{"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":"97","DOI":"10.1109\/MSP.2019.2933719","article-title":"Low-Power Neuromorphic Hardware for Signal Processing Applications: A review of architectural and system-level design approaches","volume":"36","author":"Rajendran","year":"2019","journal-title":"IEEE Signal Process. 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