To the role of the choice of the neuron model in spiking network learning on base of Spike-Timing-Dependent Plasticity. - Alexander G. Sboev; Roman B. Rybka; Alexey Serenko; Danila Vlasov; Nikolay A. Kudryashov; Vyacheslav A. Demin (BICA, 2017)

Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity. - Vyacheslav A. Demin; Dmitry V. Nekhaev (Frontiers Neuroinformatics, 2018)

Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network. - Vyacheslav A. Demin; Dmitry V. Nekhaev; Igor A. Surazhevsky; Kristina E. Nikiruy; Andrey V. Emelyanov; Sergey N. Nikolaev; Vladimir V. Rylkov; Mikhail V. Kovalchuk (Neural Networks, 2021)

Fine-tuning with local learning rules helps to compress and accelerate spiking neural networks without accuracy loss. - Dmitry V. Nekhaev; Vyacheslav A. Demin (Neural Comput. Appl., 2022)

Combination of Organic-Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification. - Anna N. Matsukatova; Nikita V. Prudnikov; Vsevolod A. Kulagin; Silvia Battistoni; Anton A. Minnekhanov; Andrey D. Trofimov; Aleksandr A. Nesmelov; Sergey A. Zavyalov; Yulia N. Malakhova; Matteo Parmeggiani; Alberto Ballesio; Simone Luigi Marasso; et al. (Adv. Intell. Syst., 2023)

Memristor-based spiking neural network with online reinforcement learning. - Danila Vlasov; Anton A. Minnekhanov; Roman B. Rybka; Yury Davydov; Alexander G. Sboev; Alexey Serenko; Alexander Ilyasov; Vyacheslav A. Demin (Neural Networks, 2023)