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Stable propagation of synchronous spiking in cortical neural networks

Author

Listed:
  • Markus Diesmann

    (Institute of Biology III, Albert-Ludwigs-University
    Max-Planck-Institut für Stömungsforschung)

  • Marc-Oliver Gewaltig

    (Institute of Biology III, Albert-Ludwigs-University
    Future Technology Research, Honda R&D Europe)

  • Ad Aertsen

    (Institute of Biology III, Albert-Ludwigs-University)

Abstract

The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing1,2,3,4,5 have been challenged by the assumption that these neurons operate in a noisy fashion—presumably reflecting fluctuations in synaptic input6—and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.

Suggested Citation

  • Markus Diesmann & Marc-Oliver Gewaltig & Ad Aertsen, 1999. "Stable propagation of synchronous spiking in cortical neural networks," Nature, Nature, vol. 402(6761), pages 529-533, December.
    • Handle: RePEc:nat:nature:v:402:y:1999:i:6761:d:10.1038_990101
    DOI: 10.1038/990101
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    Cited by:

    1. Gabriel Koch Ocker & Krešimir Josić & Eric Shea-Brown & Michael A Buice, 2017. "Linking structure and activity in nonlinear spiking networks," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-47, June.
    2. Wu, Yan & Wu, Liqing & Zhu, Yuan & Yi, Ming & Lu, Lulu, 2024. "Enhancing weak signal propagation by intra- and inter-layer global couplings in a feedforward network," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Yao, Chenggui & Ma, Jun & He, Zhiwei & Qian, Yu & Liu, Liping, 2019. "Transmission and detection of biharmonic envelope signal in a feed-forward multilayer neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 797-806.
    4. Qin, Ying-Mei & Che, Yan-Qiu & Zhao, Jia, 2018. "Effects of degree distributions on signal propagation in noisy feedforward neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 763-774.
    5. Yao, Chenggui & Yao, Yuangen & Qian, Yu & Xu, Xufan, 2022. "Temperature-controlled propagation of spikes in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    6. Samira Abbasi & Amber E Hudson & Selva K Maran & Ying Cao & Ataollah Abbasi & Detlef H Heck & Dieter Jaeger, 2017. "Robust transmission of rate coding in the inhibitory Purkinje cell to cerebellar nuclei pathway in awake mice," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-25, June.
    7. Richard Naud & Wulfram Gerstner, 2012. "Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-14, October.
    8. Andrey Molyakov, 2019. "Mathematical Modeling of Neurodynamic Systems- Solving DIS-Tasks Using Massive-Multithread Supercomputers," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 21(5), pages 16159-16162, October.
    9. Ding, Qianming & Wu, Yong & Li, Tianyu & Yu, Dong & Jia, Ya, 2023. "Metabolic energy consumption and information transmission of a two-compartment neuron model and its cortical network," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    10. Emiliano Torre & Carlos Canova & Michael Denker & George Gerstein & Moritz Helias & Sonja Grün, 2016. "ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-34, July.
    11. Herbert Jaeger & Beatriz Noheda & Wilfred G. Wiel, 2023. "Toward a formal theory for computing machines made out of whatever physics offers," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    12. Montangie, Lisandro & Montani, Fernando, 2015. "Quantifying higher-order correlations in a neuronal pool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 388-400.
    13. Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja Grün, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
    14. Elliott Capek & Tiago L. Ribeiro & Patrick Kells & Keshav Srinivasan & Stephanie R. Miller & Elias Geist & Mitchell Victor & Ali Vakili & Sinisa Pajevic & Dante R. Chialvo & Dietmar Plenz, 2023. "Parabolic avalanche scaling in the synchronization of cortical cell assemblies," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Tom Tetzlaff & Moritz Helias & Gaute T Einevoll & Markus Diesmann, 2012. "Decorrelation of Neural-Network Activity by Inhibitory Feedback," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-29, August.
    16. Miguel Aguilera & Masanao Igarashi & Hideaki Shimazaki, 2023. "Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    17. Brot, Hilla & Muchnik, Lev & Goldenberg, Jacob & Louzoun, Yoram, 2012. "Feedback between node and network dynamics can produce real-world network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6645-6654.
    18. Feng-Sheng Tsai & Yi-Li Shih & Chin-Tzong Pang & Sheng-Yi Hsu, 2019. "Formulation of Pruning Maps with Rhythmic Neural Firing," Mathematics, MDPI, vol. 7(12), pages 1-15, December.
    19. Mizusaki, Beatriz E.P. & Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2017. "Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 279-286.
    20. Moritz Helias & Tom Tetzlaff & Markus Diesmann, 2014. "The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-21, January.

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