1. 2024
  2. Forecasting coherence resonance in a stochastic Fitzhugh–Nagumo neuron model using reservoir computing

    Hramov, A., Kulagin, N., Andreev, A. & Pisarchik, A., 2024, In: Chaos, Solitons and Fractals. 178, 114354.

    Research output: Contribution to journalArticlepeer-review

  3. 2023
  4. Coherence resonance in neural networks: Theory and experiments

    Pisarchik, A. N. & Hramov, A. E., 1 Feb 2023, In: Physics Reports. 1000, p. 1-57 57 p.

    Research output: Contribution to journalReview articlepeer-review

  5. The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder

    Pitsik, E. N., Maximenko, V. A., Kurkin, S. A., Sergeev, A. P., Stoyanov, D., Paunova, R., Kandilarova, S., Simeonova, D. & Hramov, A. E., 1 Feb 2023, In: Chaos, Solitons and Fractals. 167, 113041.

    Research output: Contribution to journalArticlepeer-review

  6. Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data

    Boronina, A., Maksimenko, V. & Hramov, A. E., 2023, In: Mathematics. 11, 11, 2515.

    Research output: Contribution to journalArticlepeer-review

  7. 2022
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