IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v139y2020ics0960077920304586.html
   My bibliography  Save this article

Stimulus classification using chimera-like states in a spiking neural network

Author

Listed:
  • Andreev, Andrey V.
  • Ivanchenko, Mikhail V.
  • Pisarchik, Alexander N.
  • Hramov, Alexander E.

Abstract

A complex network of bistable Hodgkin-Huxley (HH) neurons with excitatory coupling can exhibit a partially spiking chimera behavior. We propose to use this chimera-like state for classification of the entering stimulus amplitude in the neural network with coexisting resting and spiking states. Due to different additive noise applied to each neuron in the network, the neurons are nonidentical. Therefore, depending on the amplitude of the external current, a part of the neurons stays in the resting state, while another part oscillates. Keeping fixed the coupling strength between neurons inside the network, we train the neural network on external pulses with two different amplitudes to adjust the coupling strength between the network neurons and two output neurons. We consider two variants of the classifier, in the presence and in the absence of inhibitory coupling between output neurons, and study how the output neurons respond to the external pulses of different amplitudes. The accuracy of the proposed classifier reaches 100% when the output neurons are inhibitory coupled, so that only one of these neurons is activated.

Suggested Citation

  • Andreev, Andrey V. & Ivanchenko, Mikhail V. & Pisarchik, Alexander N. & Hramov, Alexander E., 2020. "Stimulus classification using chimera-like states in a spiking neural network," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304586
    DOI: 10.1016/j.chaos.2020.110061
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920304586
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110061?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bandyopadhyay, Abhirup & Kar, Samarjit, 2018. "Impact of network structure on synchronization of Hindmarsh–Rose neurons coupled in structured network," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 194-212.
    2. Runnova, Anastasiya E. & Hramov, Alexander E. & Grubov, Vadim V. & Koronovskii, Alexey A. & Kurovskaya, Maria K. & Pisarchik, Alexander N., 2016. "Theoretical background and experimental measurements of human brain noise intensity in perception of ambiguous images," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 201-206.
    3. E. V. Pankratova & A. V. Polovinkin & E. Mosekilde, 2005. "Resonant activation in a stochastic Hodgkin-Huxley model: Interplay between noise and suprathreshold driving effects," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 45(3), pages 391-397, June.
    4. Nefeli Dimitra Tsigkri-DeSmedt & Johanne Hizanidis & Eckehard Schöll & Philipp Hövel & Astero Provata, 2017. "Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(7), pages 1-11, July.
    5. Vladimir A. Maksimenko & Semen A. Kurkin & Elena N. Pitsik & Vyacheslav Yu. Musatov & Anastasia E. Runnova & Tatyana Yu. Efremova & Alexander E. Hramov & Alexander N. Pisarchik, 2018. "Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity," Complexity, Hindawi, vol. 2018, pages 1-10, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guo, Lei & Guo, Minxin & Wu, Youxi & Xu, Guizhi, 2023. "Specific neural coding of fMRI spiking neural network based on time coding," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Klinshov, Vladimir V. & Kovalchuk, Andrey V. & Franović, Igor & Perc, Matjaž & Svetec, Milan, 2022. "Rate chaos and memory lifetime in spiking neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Rajagopal, Karthikeyan & Karthikeyan, Anitha, 2022. "Spiral waves and their characterization through spatioperiod and spatioenergy under distinct excitable media," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Min & Fang, Yuwen & Luo, Yuhui & Yang, Fengzao & Zeng, Chunhua & Duan, Wei-Long, 2019. "Influence of non-Gaussian noise on the coherent feed-forward loop with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 129(C), pages 46-55.
    2. Yilmaz, Ergin, 2014. "Impacts of hybrid synapses on the noise-delayed decay in scale-free neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 66(C), pages 1-8.
    3. Lu, Lulu & Ge, Mengyan & Xu, Ying & Jia, Ya, 2019. "Phase synchronization and mode transition induced by multiple time delays and noises in coupled FitzHugh–Nagumo model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    4. Andreev, Andrey V. & Makarov, Vladimir V. & Runnova, Anastasija E. & Pisarchik, Alexander N. & Hramov, Alexander E., 2018. "Coherence resonance in stimulated neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 80-85.
    5. Li, Y.L. & Chen, B. & Chen, G.Q., 2020. "Carbon network embodied in international trade: Global structural evolution and its policy implications," Energy Policy, Elsevier, vol. 139(C).
    6. Branislav Rehák & Volodymyr Lynnyk, 2021. "Synchronization of a Network Composed of Stochastic Hindmarsh–Rose Neurons," Mathematics, MDPI, vol. 9(20), pages 1-16, October.
    7. Li, Yingying & Wei, Zhouchao & Zhang, Wei & Perc, Matjaž & Repnik, Robert, 2019. "Bogdanov–Takens singularity in the Hindmarsh–Rose neuron with time delay," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 180-188.
    8. Ramasamy, Mohanasubha & Devarajan, Subhasri & Kumarasamy, Suresh & Rajagopal, Karthikeyan, 2022. "Effect of higher-order interactions on synchronization of neuron models with electromagnetic induction," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    9. Aghababaei, Sajedeh & Balaraman, Sundarambal & Rajagopal, Karthikeyan & Parastesh, Fatemeh & Panahi, Shirin & Jafari, Sajad, 2021. "Effects of autapse on the chimera state in a Hindmarsh-Rose neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    10. Yao, Chenggui & Sun, JianQiang & Jin, Jun & Shuai, Jianwei & Li, Xiang & Yao, Yuangen & Xu, Xufan, 2023. "The power law statistics of the spiking timing in a neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    11. Alexandros Rontogiannis & Astero Provata, 2021. "Chimera states in FitzHugh–Nagumo networks with reflecting connectivity," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(5), pages 1-12, May.
    12. Baysal, Veli & Yılmaz, Ergin, 2021. "Chaotic Signal Induced Delay Decay in Hodgkin-Huxley Neuron," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    13. Sebastian Jenderny & Karlheinz Ochs & Philipp Hövel, 2023. "A memristor-based circuit approximation of the Hindmarsh–Rose model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(8), pages 1-10, August.
    14. Uzuntarla, Muhammet & Uzun, Rukiye & Yilmaz, Ergin & Ozer, Mahmut & Perc, Matjaž, 2013. "Noise-delayed decay in the response of a scale-free neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 202-208.
    15. Binghui Li & Yuehan Yang, 2022. "Undirected and Directed Network Analysis of the Chinese Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1155-1173, October.
    16. Xiao, Fangli & Fu, Ziying & Jia, Ya & Yang, Lijian, 2023. "Resonance effects in neuronal-astrocyte model with ion channel blockage," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    17. Mengxin Liu & Wenyuan Tao & Xiao Zhang & Yi Chen & Jie Li & Chung-Ming Own, 2019. "GO Loss: A Gaussian Distribution-Based Orthogonal Decomposition Loss for Classification," Complexity, Hindawi, vol. 2019, pages 1-10, December.
    18. Moskalenko, Olga I. & Koronovskii, Alexey A. & Zhuravlev, Maksim O. & Hramov, Alexander E., 2018. "Characteristics of noise-induced intermittency," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 269-275.
    19. Zhao, Yiran & Gao, Xiangyun & An, Haizhong & Xi, Xian & Sun, Qingru & Jiang, Meihui, 2020. "The effect of the mined cobalt trade dependence Network's structure on trade price," Resources Policy, Elsevier, vol. 65(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304586. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.