Abstract
Plasticity of synaptic weights is usually supposed the foundation of learning and long-term memory in biological neural networks. Mathematical models of both biological and artificial neural networks reflect this vision. Little attention is paid to the role spike propagation delays play in information processing and learning. We propose a model of myelin plasticity which controls the efficiency of spikes propagation along axons. A neuron modifies the myelin sheath thickness of its input axons to achieve better synchrony of incoming spikes. Synchronous input spikes cause higher postsynaptic response which leads to higher spike generation probability. We show that the axonal delay plasticity model may be used to train a network recognize input patterns even when synaptic weights remain fixed. The delay plasticity approach may be a useful augmentation of spiking neural networks used in neuromorphic computing.
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
References
Dutta, D.J., Woo, D.H., Lee, P.R., Pajevic, S., Bukalo, O., Huffman, W.C., Wake, H., Basser, P.J., SheikhBahaei, S., Lazarevic, V., et al.: Regulation of myelin structure and conduction velocity by perinodal astrocytes. Proc. Natl. Acad. Sci. 115(46), 11832–11837 (2018)
Ferraina, S., Paré, M., Wurtz, R.H.: Comparison of cortico-cortical and cortico-collicular signals for the generation of saccadic eye movements. J. Neurophysiol. 87(2), 845–858 (2002)
Fields, R.D., Bukalo, O.: Myelin makes memories. Nat. Neurosci. 23(4), 469–470 (2020)
FitzGibbon, T., Nestorovski, Z.: Human intraretinal myelination: Axon diameters and axon/myelin thickness ratios. Indian J. Ophthalmol. 61(10), 567 (2013)
Hull, J.J.: A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 16(5), 550–554 (1994)
Khoshkhou, M., Montakhab, A.: Spike-timing-dependent plasticity with axonal delay tunes networks of izhikevich neurons to the edge of synchronization transition with scale-free avalanches. Front. Syst. Neurosci. 13, 73 (2019)
London, M., Häusser, M.: Dendritic computation. Annu. Rev. Neurosci. 28, 503–532 (2005)
Maass, W., Schmitt, M.: On the complexity of learning for spiking neurons with temporal coding. Inf. Comput. 153(1), 26–46 (1999)
Nadafian, A., Ganjtabesh, M.: Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks. arXiv preprint arXiv:2011.09380 (2020)
Oubari, O.: Precise timing and computationally efficient learning in neuromorphic systems. Ph.D. thesis, Sorbonne université (2020)
Paugam-Moisy, H., Martinez, R., Bengio, S.: Delay learning and polychronization for reservoir computing. Neurocomputing 71(7–9), 1143–1158 (2008)
Talidou, A., Frankland, P.W., Mabbott, D., Lefebvre, J.: Learning to be on Time: Temporal Coordination of Neural Dynamics by Activity-Dependent Myelination. bioRxiv, pp. 2021–08 (2021)
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
Chaplinskaia, N., Bazenkov, N. (2024). Axonal Myelination as a Mechanism for Unsupervised Learning in Spiking Neural Networks. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-50381-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50380-1
Online ISBN: 978-3-031-50381-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)