Abstract
In this work we test some state of the art works in Spiking Neural Networks (SNN) to train them using surrogate gradients and using different loss functions to perform classification tasks using recurrent SNN and with our own datasets. We show that this kind of networks can accomplish in a good way the classifications tasks, but can not generalize the features of the incoming images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bellec, G., et al.: A solution to the learning dilemma for recurrent networks of spiking neurons. Nat. Commun. 11(1), 3625 (2020)
Eshraghian, J.K.: snnTorch documentation (2021). https://snntorch.readthedocs.io/en/latest/. Accessed 08 Feb 2024
Eshraghian, J.K., et al.: Training spiking neural networks using lessons from deep learning. In: Proceedings of the IEEE (2023)
Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., Tian, Y.: Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2661–2671 (2021)
Hasanpour, S.H., Rouhani, M., Fayyaz, M., Sabokrou, M.: Lets keep it simple, using simple architectures to outperform deeper and more complex architectures. arXiv preprint arXiv:1608.06037 (2016)
Modha, D.S., et al.: Neural inference at the frontier of energy, space, and time. Science 382(6668), 329–335 (2023)
Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36(6), 51–63 (2019)
Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9, 437 (2015)
Reynolds, J.J., et al.: A comparison of neuromorphic classification tasks. In: Proceedings of the International Conference on Neuromorphic Systems, pp. 1–8 (2018)
Schuman, C.D., et al.: Opportunities for neuromorphic computing algorithms and applications. Nature Comput. Sci. 2(1), 10–19 (2022)
Stöckl, C., Maass, W.: Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. Nat. Mach. Intell. 3(3), 230–238 (2021)
Yamazaki, K., Vo-Ho, V.K., Bulsara, D., Le, N.: Spiking neural networks and their applications: a review. Brain Sci. 12(7), 863 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cureño Ramirez, A., García Morgado, B., de la Fraga, L.G. (2024). Image Classification with Recurrent Spiking Neural Networks. In: Mezura-Montes, E., Acosta-Mesa, H.G., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2024. Lecture Notes in Computer Science, vol 14755. Springer, Cham. https://doi.org/10.1007/978-3-031-62836-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-62836-8_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62835-1
Online ISBN: 978-3-031-62836-8
eBook Packages: Computer ScienceComputer Science (R0)
