Abstract
Artificial neural network (ANN) research is inspired by how information is dynamically and massively processed in parallel by biological neural networks. Spiking neural networks (SNNs) are versions of artificial neural networks that are more biologically realistic than commonly used static models. As in actual brains, neurons signal each other through current spikes (rather than constant inputs, as in conventional ANNs), and spike timing plays a key role in SNN functioning. In this work, we give an overview of SNNs, and we describe and implement three different mathematical models (integrate and fire, leaky integrate and fire, conductance-based) using the Brian2 software simulator. We also describe the training of SNNs using the spike-timing-dependent plasticity (STDP) algorithm, and discuss an experiment by Diehl and Cook that shows the ability of SNNs to learn to distinguish handwritten digits. Lastly, we describe a few available software SNN simulators and hardware SNN implementations with possible practical applications of SNNs.
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
U. Güçlü, M. van Gerven, Probing human brain function with artificial neural networks, in Comput. Model. Brain Behav., ed. by A.A. Moustafa, (2017), pp. 413--423. https://doi.org/10.1002/9781119159193.ch30
D. Soni, Spiking neural networks, the next generation of machine learning, Data Science and Machine Learning (2010). https://towardsdatascience.com/spiking-neural-networks-the-next-generation-of-machine-learning-84e167f4eb2b. Accessed 13 Oct 2019
H. Hazan et al., BindsNET: A machine learning-oriented spiking neural networks library in python. Front. Neuroinform. 12, 1–18 (2018)
T. Mondeel, Modelling Neuronal Excitation: The Hodgkin-Huxley Model (2012)
J.B. Baladron, D.F. Javier, O. Faugeras, J. Touboul, Mean-field description and propagation of chaos in networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons. J. Math. Neurosci. 2(1), 1–67 (2012). https://doi.org/10.1186/2190-8567-2-10
J.L. Hindmarsh, R.M. Rose, A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. Lond. B Biol. Sci. 221(1222), 87–102 (1984)
L.F. Abbott, Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Res. Bull. 50(5–6), 303–304 (1999)
P. Diehl, M. Cook, Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015)
Ş. Mihalaş, E. Niebur, A generalized linear integrate-and-fire neural model produces diverse spiking behaviors. Neural Comput. 21(3), 704–718 (2009)
R.D. Vilela, B. Lindner, Comparative study of different integrate-and-fire neurons: Spontaneous activity, dynamical response, and stimulus-induced correlation. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 80(3), 1–12 (2009)
A. Tavanaei, A. Maida, BP-STDP: Approximating backpropagation using spike timing dependent plasticity. Neurocomputing 330, 39–47 (2019)
H. Markram, W. Gerstner, P.J. Sjöström, Spike-timing-dependent plasticity: A comprehensive overview. Front. Synaptic Neurosci., 2–5 (2012)
G. Bi, M. Poo, Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24(1), 139–166 (2001)
M. Stimberg, Brian 2 documentation—Brian 2 2.2.2.1 documentation (2016)
Y. LeCun, C. Cortes, C. Burges, The MNIST database of handwritten digits. Courant Inst. Math. Sci., 1–10 (1998)
D. Goodman, Brian: A simulator for spiking neural networks in Python. Front. Neuroinform. 2 (2008)
R. Brette, D.F.M. Goodman, Vectorized algorithms for spiking neural network simulation. Neural Comput. 23(6), 1503–1535 (2011)
Neuron, Welcome to the community of NEURON users and developers (2018). http://www.neuron.yale.edu/neuron/. Accessed 26 Sept 2019
Genesis, GENESIS Resources (2019). http://genesis-sim.org/. Accessed 26 Sept 2019
M.-O. Gewaltig, M. Diesmann, NEST (Neural simulation tool). Scholarpedia 2(4), 1430 (2007)
M. Smith, Self aware patterns, SelfAwarePatterns (2018). https://selfawarepatterns.com/2019/05/08/brain-inspired-hardware/. Accessed 30 Sept 2019
D. S. Modha, Introducing a brain-inspired computer: TrueNorth’s neurons to revolutionize system architecture, IBM Research (2015). http://www.research.ibm.com/articles/brain-chip.shtml. Accessed 30 Sept 2019
A. S. Cassidy et al., Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores, in Proceedings of the International Joint Conference on Neural Networks (2013)
F. Akopyan et al., TrueNorth: Design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Des. Integr. Circuits Syst. 34(10), 1537–1557 (2015)
M. Feldman, IBM finds killer app for truenorth neuromorphic chip, TOP500 Supercomputer Sites (2016). https://www.top500.org/news/ibm-finds-killer-app-for-truenorth-neuromorphic-chip/. Accessed 30 Sept 2019
T. Hwu, J. Isbell, N. Oros, and J. Krichmar, A self-driving robot using deep convolutional neural networks on neuromorphic hardware, in Proceedings of the International Joint Conference on Neural Networks, vol 2017 (2017), pp. 635–641
N. Qiao et al., A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9 (2015). https://doi.org/10.3389/fnins.2015.00141
B.V. Benjamin et al., Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102(5), 699–716 (2014)
E. Painkras et al., SpiNNaker: A 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid State Circuits 48(8), 1943–1953 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ojiugwo, C.N., Abdallah, A.B., Thron, C. (2020). Simulation of Biological Learning with Spiking Neural Networks. In: Subair, S., Thron, C. (eds) Implementations and Applications of Machine Learning. Studies in Computational Intelligence, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-030-37830-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-37830-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37829-5
Online ISBN: 978-3-030-37830-1
eBook Packages: EngineeringEngineering (R0)