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Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time

Nature Machine Intelligence volume 5, pages 518–527 (2023)Cite this article

Subjects

A preprint version of the article is available at arXiv.

Abstract

With recent advances in learning algorithms, recurrent networks of spiking neurons are achieving performance that is competitive with vanilla recurrent neural networks. However, these algorithms are limited to small networks of simple spiking neurons and modest-length temporal sequences, as they impose high memory requirements, have difficulty training complex neuron models and are incompatible with online learning. Here, we show how the recently developed Forward-Propagation Through Time (FPTT) learning combined with novel liquid time-constant spiking neurons resolves these limitations. Applying FPTT to networks of such complex spiking neurons, we demonstrate online learning of exceedingly long sequences while outperforming current online methods and approaching or outperforming offline methods on temporal classification tasks. The efficiency and robustness of FPTT enable us to directly train a deep and performant spiking neural network for joint object localization and recognition, demonstrating the ability to train large-scale dynamic and complex spiking neural network architectures.

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Fig. 1: FPTT and LTC spiking neurons.
Fig. 2: Performance evaluation of SNNs.
Fig. 3: Spiking-YOLO-v4 (SPYv4).

Data availability

This paper utilized publicly available datasets. The MNIST dataset can be accessed at http://yann.lecun.com/exdb/mnist/, while the Fashion MNIST dataset is available at https://github.com/zalandoresearch/fashion-mnist. The DVS-GESTURE dataset can be download at https://research.ibm.com/interactive/dvsgesture/, and the DVS-Cifar10 dataset can be obtained from https://figshare.com/articles/dataset/CIFAR10-DVS_New/4724671/2. The PASCAL VOC07 and VOC2012 datasets are publicly available at http://host.robots.ox.ac.uk/pascal/VOC/index.html. It is important to note that no software was used in the collection of data. Source data are provided with this paper.

Code availability

The code used in the study is publicly available from the GitHub repository https://github.com/byin-cwi/sFPTT/tree/v1.0.0 and Zenodo45 (https://zenodo.org/record/7498559).

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Acknowledgements

B.Y. is supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Toegepaste en Technische Wetenschappen (NWO-TTW) Programme ‘Efficient Deep Learning’ (EDL) P16-25. S.M.B. is supported by the European Union (grant agreement 7202070 ‘Human Brain Project’). The authors are grateful to H. Corporaal for reading the manuscript and providing constructive remarks.

Author information

Authors and Affiliations

  1. CWI, Machine Learning Group, Amsterdam, The Netherlands

    Bojian Yin & Sander M. Bohté

  2. Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Federico Corradi

  3. Stichting IMEC Netherlands, Eindhoven, The Netherlands

    Federico Corradi

  4. Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands

    Sander M. Bohté

  5. Faculty of Science and Engineering, Rijksuniversiteit Groningen, Groningen, The Netherlands

    Sander M. Bohté

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  1. Bojian Yin
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  3. Sander M. Bohté
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Contributions

B.Y., F.C. and S.M.B. conceived the experiments. B.Y. conducted the experiments. B.Y., F.C. and S.M.B. analysed the results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Bojian Yin.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Catherine Schuman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary information

Supplementary Figs. 1–6, Tables 1–5 and Appendix A for FPTT theory.

Reporting Summary

Source data

Source Data Fig. 2

Statistical source data for Fig. 2b,c.

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Yin, B., Corradi, F. & Bohté, S.M. Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time. Nat Mach Intell 5, 518–527 (2023). https://doi.org/10.1038/s42256-023-00650-4

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