activity
20182023
most citedDelay-induced self-oscillation excitation in the FitzHugh-Nagumo model: regular and chaotic dynamics

18 citations · 40 across the 4 of their papers we have counts for

collaborators

6 papers

nlin.AO2023★ 7 cited

Multiplexing-based control of wavefront propagation: the interplay of inter-layer coupling, asymmetry and noise

Vladimir V. Semenov, Sarika Jalan, Anna Zakharova

We show how multiplexing influences propagating fronts in multilayer networks of coupled bistable oscillators. Using numerical simulation, we investigate both deterministic and noi…

physics.optics2023★ 6 cited

Deterministic and stochastic coarsening control in optically-addressed spatial light modulators subject to optical feedback

Vladimir V. Semenov, Xavier Porte, Laurent Larger +1

Phase separation accompanied by further domain growth and coarsening is a phenomenon common to a broad variety of dynamical systems. In this connection, controlling such processes…

nlin.AO2023★ 18 cited

Delay-induced self-oscillation excitation in the FitzHugh-Nagumo model: regular and chaotic dynamics

Vladimir V. Semenov, Andrei V. Bukh, Nadezhda Semenova

The stochastic FitzHugh-Nagumo model with time delayed-feedback is often studied in excitable regime to demonstrate the time-delayed control of coherence resonance. Here, we show t…

nlin.AO2022★ 9 cited

Stochastic control of spiking activity bump expansion: monotonic and resonant phenomena

Vladimir V. Semenov, Anna Zakharova

We consider spatially localized spiking activity patterns, so-called bumps, in ensembles of bistable spiking oscillators. The bistability consists in the coexistence of self-sustai…

cs.CV2018

A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training

Viacheslav Dudar, Giovanni Chierchia, Emilie Chouzenoux +2

In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to the cost function in a low…

cs.CV2018

Use of symmetric kernels for convolutional neural networks

Viacheslav Dudar, Vladimir Semenov

At this work we introduce horizontally symmetric convolutional kernels for CNNs which make the network output invariant to horizontal flips of the image. We also study other types…