V. A. Demin

发表

USING MULTILEVEL SIMULATION MODELS OF REGENERATING PROCESSES WHEN EVALUATING RHYTHMICITY OF PRODUCTION PROCESSES

V. A. Demin, I. A. Eskova, A. B. Nikolaev, 2013 .

Specific features, problems and content of the management mechanism of enterprises of the electrical engineering industry

V. A. Demin, 2014 .

Model for Determining the Optimal Trajectory of Movement of Consignments

V. A. Demin, D. B. Efimenko, S. Eurich, 2019 .

Radiofrequency sensors in problems of measuring liquid supplies in tanks of space vehicles

V. A. Demin, B. V. Lunkin, Valerjan Ya. Fateev, 2013, Autom. Remote. Control..

Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications

V. A. Demin, O. Gorshkov, P. Dimitrakis, 2019, Microelectronic Engineering.

Fine-tuning with local learning rules helps to compress and accelerate spiking neural networks without accuracy loss

V. A. Demin, D. Nekhaev, 2022, Neural Computing and Applications.

SYNAPTIC PLASTICITY OF MEMRISTIVE STRUCTURES BASED ON POLY-P-XYLYLENE

V. A. Demin, V. Rylkov, A. Emelyanov, 2019, Nanotechnologies in Russia.

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100−x Nanocomposite Memristors

V. A. Demin, A. Emelyanov, A. Bugaev, 2022, Nanomaterials.

Neuromorphic elements and systems as the basis for the physical implementation of artificial intelligence technologies

A. V. Emelyanov, Vyacheslav A. Demin, Pavel K. Kashkarov, 2016 .

Scalable nanocomposite parylene-based memristors: Multifilamentary resistive switching and neuromorphic applications

V. A. Demin, P. Kashkarov, A. Emelyanov, 2022, Nano Research.

Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network

Victor B. Kazantsev, Bernardo Spagnolo, K. E. Nikiruy, 2021 .

Band gaps in jagged and straight graphene nanoribbons tunable by an external electric field

V. A. Saroka, V. A. Demin, L. Chernozatonskii, 2015, Journal of physics. Condensed matter : an Institute of Physics journal.

Impact of Chronic Oral Administration of Gold Nanoparticles on Cognitive Abilities of Mice

V. A. Demin, D. Rogatkin, M. Frontasyeva, 2021, Physics of Particles and Nuclei Letters.

A Precise Algorithm of Memristor Switching to a State with Preset Resistance

K. E. Nikiruy, A. V. Emelyanov, Vyacheslav A. Demin, 2018 .

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

V. A. Demin, V. Erokhin, S. Marasso, 2023, Adv. Intell. Syst..

Nanocomposite parylene-C memristors with embedded Ag nanoparticles for biomedical data processing

V. A. Demin, A. Emelyanov, A. Minnekhanov, 2022, Organic Electronics.

Multi-Terminal Nonwoven Stochastic Memristive Devices Based on Polyamide-6 and Polyaniline for Neuromorphic Computing

V. A. Demin, V. Erokhin, A. Emelyanov, 2023, Biomimetics.

Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing

V. A. Demin, A. Vasiliev, A. Emelyanov, 2022, Nanotechnology.

Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network

V. A. Demin, V A Demin, D V Nekhaev, 2020, Neural Networks.

Associative STDP-like learning of neuromorphic circuits based on polyaniline memristive microdevices

Victor Erokhin, Vyacheslav A. Demin, Nikita Prudnikov, 2020, Journal of Physics D: Applied Physics.

Determination of Silver Nanoparticle Concentration Ratio in the Blood and Brain of Rats for Different Administration Routes

V. A. Demin, V. Demin, A. Antsiferova, 2017 .

Hardware elementary perceptron based on polyaniline memristive devices

A. V. Emelyanov, Vyacheslav A. Demin, Pavel K. Kashkarov, 2015 .

Competitive Maximization of Neuronal Activity in Convolutional Recurrent Spiking Neural Networks

Vyacheslav A. Demin, V. A. Demin, Dmitry Nekhaev, 2019, Studies in Computational Intelligence.

Memristive circuit-based model of central pattern generator to reproduce spinal neuronal activity in walking pattern

V. A. Demin, Igor A. Lavrov, V. V. Erokhin, 2023, Frontiers in Neuroscience.

Spike-timing-dependent plasticity of polyaniline-based memristive element

A. V. Emelyanov, Vyacheslav A. Demin, D. A. Lapkin, 2018 .

Polyaniline‐Based Memristive Devices as Key Elements of Robust Reservoir Computing for Image Classification

V. A. Demin, V. V. Erokhin, A. Emelyanov, 2023, physica status solidi (a).

Conductance Quantization in Memristive Structures Based on Poly-p-Xylylene

V. A. Demin, V. Rylkov, A. Emelyanov, 2020, Semiconductors.