IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-023-44614-z.html
   My bibliography  Save this article

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

Author

Listed:
  • Hanle Zheng

    (Tsinghua University)

  • Zhong Zheng

    (Tsinghua University)

  • Rui Hu

    (Tsinghua University)

  • Bo Xiao

    (Tsinghua University)

  • Yujie Wu

    (Graz University of Technology)

  • Fangwen Yu

    (Tsinghua University)

  • Xue Liu

    (Tsinghua University)

  • Guoqi Li

    (Chinese Academy of Sciences)

  • Lei Deng

    (Tsinghua University)

Abstract

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Suggested Citation

  • Hanle Zheng & Zhong Zheng & Rui Hu & Bo Xiao & Yujie Wu & Fangwen Yu & Xue Liu & Guoqi Li & Lei Deng, 2024. "Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44614-z
    DOI: 10.1038/s41467-023-44614-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44614-z
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-44614-z?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
    ---><---

    References listed on IDEAS

    as
    1. Attila Losonczy & Judit K. Makara & Jeffrey C. Magee, 2008. "Compartmentalized dendritic plasticity and input feature storage in neurons," Nature, Nature, vol. 452(7186), pages 436-441, March.
    2. Jacopo Bono & Claudia Clopath, 2017. "Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level," Nature Communications, Nature, vol. 8(1), pages 1-17, December.
    3. Michalis Pagkalos & Spyridon Chavlis & Panayiota Poirazi, 2023. "Introducing the Dendrify framework for incorporating dendrites to spiking neural networks," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Kwabena Boahen, 2022. "Dendrocentric learning for synthetic intelligence," Nature, Nature, vol. 612(7938), pages 43-50, December.
    5. Nicolas Perez-Nieves & Vincent C. H. Leung & Pier Luigi Dragotti & Dan F. M. Goodman, 2021. "Neural heterogeneity promotes robust learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    6. Rong Zhao & Zheyu Yang & Hao Zheng & Yujie Wu & Faqiang Liu & Zhenzhi Wu & Lukai Li & Feng Chen & Seng Song & Jun Zhu & Wenli Zhang & Haoyu Huang & Mingkun Xu & Kaifeng Sheng & Qianbo Yin & Jing Pei &, 2022. "A framework for the general design and computation of hybrid neural networks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    7. Alexandra Tzilivaki & George Kastellakis & Panayiota Poirazi, 2019. "Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    8. Guillaume Bellec & Franz Scherr & Anand Subramoney & Elias Hajek & Darjan Salaj & Robert Legenstein & Wolfgang Maass, 2020. "A solution to the learning dilemma for recurrent networks of spiking neurons," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    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. Michalis Pagkalos & Spyridon Chavlis & Panayiota Poirazi, 2023. "Introducing the Dendrify framework for incorporating dendrites to spiking neural networks," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Linda Judák & Balázs Chiovini & Gábor Juhász & Dénes Pálfi & Zsolt Mezriczky & Zoltán Szadai & Gergely Katona & Benedek Szmola & Katalin Ócsai & Bernadett Martinecz & Anna Mihály & Ádám Dénes & Bálint, 2022. "Sharp-wave ripple doublets induce complex dendritic spikes in parvalbumin interneurons in vivo," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Joshua M. Diamond & Julio I. Chapeton & Weizhen Xie & Samantha N. Jackson & Sara K. Inati & Kareem A. Zaghloul, 2024. "Focal seizures induce spatiotemporally organized spiking activity in the human cortex," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Yu, Dong & Wang, Guowei & Ding, Qianming & Li, Tianyu & Jia, Ya, 2022. "Effects of bounded noise and time delay on signal transmission in excitable neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    6. Marcello, Salustri & Shunra, Yoshida & Ruggero, Micheletto, 2023. "Neural and axonal heterogeneity improves information transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    7. Matteo Farinella & Daniel T Ruedt & Padraig Gleeson & Frederic Lanore & R Angus Silver, 2014. "Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-21, April.
    8. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. Yu, Dong & Wu, Yong & Yang, Lijian & Zhao, Yunjie & Jia, Ya, 2023. "Effect of topology on delay-induced multiple resonances in locally driven systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    10. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    11. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    12. Balázs Ujfalussy & Tamás Kiss & Péter Érdi, 2009. "Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-16, September.
    13. Yujie Wu & Bizhao Shi & Zhong Zheng & Hanle Zheng & Fangwen Yu & Xue Liu & Guojie Luo & Lei Deng, 2024. "Adaptive spatiotemporal neural networks through complementary hybridization," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    14. Zhenrui Liao & Kevin C. Gonzalez & Deborah M. Li & Catalina M. Yang & Donald Holder & Natalie E. McClain & Guofeng Zhang & Stephen W. Evans & Mariya Chavarha & Jane Simko & Christopher D. Makinson & M, 2024. "Functional architecture of intracellular oscillations in hippocampal dendrites," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. Melika Payvand & Filippo Moro & Kumiko Nomura & Thomas Dalgaty & Elisa Vianello & Yoshifumi Nishi & Giacomo Indiveri, 2022. "Self-organization of an inhomogeneous memristive hardware for sequence learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    16. Francesco Barchi & Luca Zanatta & Emanuele Parisi & Alessio Burrello & Davide Brunelli & Andrea Bartolini & Andrea Acquaviva, 2021. "Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring," Future Internet, MDPI, vol. 13(8), pages 1-22, August.
    17. Dejan Pecevski & Lars Buesing & Wolfgang Maass, 2011. "Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-25, December.
    18. Barbara Feulner & Matthew G. Perich & Raeed H. Chowdhury & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2022. "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    19. Kim, Seil & Ogawa, Keiichi, 2024. "Who is able or unable to return to school? Exploring the short-term impact of the COVID-19 school closures on students' returning to school in Nigeria," International Journal of Educational Development, Elsevier, vol. 108(C).
    20. Ian Cone & Claudia Clopath, 2024. "Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44614-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.