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Rate chaos and memory lifetime in spiking neural networks

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  • Klinshov, Vladimir V.
  • Kovalchuk, Andrey V.
  • Franović, Igor
  • Perc, Matjaž
  • Svetec, Milan

Abstract

Rate chaos is a collective state of a neural network characterized by slow irregular fluctuations of firing rates of individual neurons. We study a sparsely connected network of spiking neurons which demonstrates three different scenarios for the emergence of rate chaos, based either on increasing the synaptic strength, increasing the synaptic integration time, or clustering of the excitatory synaptic connections. Although all the scenarios lead to collective dynamics with similar statistical features, it turns out that the implications for the computational capability of the network in performing a simple delay task are strongly dependent on the particular scenario. Namely, only the scenario involving slow dynamics of synapses results in an appreciable extension of the network's dynamic memory. In other cases, the dynamic memory remains short despite the emergence of long timescales in the neuronal spike trains.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002211
    DOI: 10.1016/j.chaos.2022.112011
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    References listed on IDEAS

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    1. Francesca Mastrogiuseppe & Srdjan Ostojic, 2017. "Intrinsically-generated fluctuating activity in excitatory-inhibitory networks," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-40, April.
    2. Omri Harish & David Hansel, 2015. "Asynchronous Rate Chaos in Spiking Neuronal Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-38, July.
    3. 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).
    4. Wilten Nicola & Claudia Clopath, 2017. "Supervised learning in spiking neural networks with FORCE training," Nature Communications, Nature, vol. 8(1), pages 1-15, December.
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    Cited by:

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    3. Wang, Zhizhi & Hu, Bing & Zhou, Weiting & Xu, Minbo & Wang, Dingjiang, 2023. "Hopf bifurcation mechanism analysis in an improved cortex-basal ganglia network with distributed delays: An application to Parkinson’s disease," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    4. Xu, Bang-Lin & Zhou, Jian-Fang & Li, Rui & Jiang, En-Hua & Yuan, Wu-Jie, 2023. "Neural dynamic transitions caused by changes of synaptic strength in heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    5. Jake Gavenas & Ueli Rutishauser & Aaron Schurger & Uri Maoz, 2024. "Slow ramping emerges from spontaneous fluctuations in spiking neural networks," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. Srinivasan, Aditya & Srinivasan, Arvind & Goodman, Michael R. & Riceberg, Justin S. & Guise, Kevin G. & Shapiro, Matthew L., 2023. "Hippocampal and Medial Prefrontal Cortex Fractal Spiking Patterns Encode Episodes and Rules," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    7. Klinshov, Vladimir V. & Kovalchuk, Andrey V. & Soloviev, Igor A. & Maslennikov, Oleg V. & Franović, Igor & Perc, Matjaž, 2024. "Extending dynamic memory of spiking neuron networks," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).

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