This document provides an introduction to spiking neural networks (SNNs) through a presentation given by Jason Tsai. It begins with an overview of the characteristics and advantages of SNNs. It then covers relevant neuroscience concepts like neurons, synapses, action potentials, Hebb's rule, and spike-timing dependent plasticity. Learning algorithms like backpropagation and STDP are introduced. Common neuron models and coding schemes are described. Finally, several neuromorphic computing platforms are discussed.

1. Jason Tsai (蔡志順)
Oct. 19, 2019 @Mozilla Community Space Taipei
*Picture adopted from
https://bit.ly/2ts8xCk
Introduction to Spiking Neural Networks

2. *Copyright Notice:
All figures in this presentation are taken from
the quoted sources as mentioned in the
respective slides and their copyright belongs
to the owners. This presentation itself adopts
Creative Commons license.

3. Neural Networks 3D Simulation
(Video demo)
*Video from https://youtu.be/3JQ3hYko51Y

4. Questions
 What are the advantages of spiking
neural networks and neuromorphic
computing?
 What are current challenges of spiking
neural networks (SNNs)?

5. Characteristics of SNNs
 Spatio-temporal
 Asynchronous
 Sparsity
 Additive weight operation*
 Energy-efficient
 Stochastic
 Robust to noise

6. Outlines
• Basic neuroscience
• Learning algorithms
• Neuron models
• Neural encoding schemes
• Neuromorphic platforms

7. Prerequisite
Neuroscience

8. Nerve Cell
(Neuron)
*Figure adopted from Eric R. Kandel, et.al. Principles of Neural Science, Fifth Edition.
McGraw-Hill Education. 2013. Page 22.

9. Synapse
*Figure adopted from https://bit.ly/2ycOmcq
(ROC means receptor-operated channels)

10. Neuron’s Spike: Action Potential
*Figure adopted from https://en.wikipedia.org/wiki/Action_potential &
The front cover of “Spikes: Exploring the Neural Code (1999)”

11. EPSP / IPSP
*Figure adopted from https://bit.ly/2OgAx7z

12. The Effect of Presynaptic Spikes on
Postsynaptic Neuron
*Figure adopted from Wulfram Gerstner & Werner M. Kistler. Spiking Neuron Models:
Single Neurons, Populations, Plasticity. Cambridge University Press. 2002. Page 5.

13. Hebb’s Learning Postulate
 "When an axon of cell A is near enough to excite a cell B and
repeatedly or persistently takes part in firing it, some growth
process or metabolic change takes place in one or both cells such
that A's efficiency, as one of the cells firing B, is increased.“*
* Refer to Donald O. Hebb, The Organization of Behavior: A Neuropsychological Theory. 1949 & 2002. Page 62.
 Causality
 Repetition

14. Long-Term Potentiation (LTP) / Long-
Term Depression (LTD)
 LTP is a long-lasting, activity-dependent increase in synaptic
strength that is a leading candidate as a cellular mechanism
contributing to memory formation in mammals in a very
broadly applicable sense.*
* Refer to J. David Sweatt. Mechanisms of Memory, Second Edition. Academic Press. 2010. Page 112.

15. Synaptic Plasticity
*Figure adopted from Wulfram Gerstner & Werner M. Kistler. Spiking Neuron Models:
Single Neurons, Populations, Plasticity. Cambridge University Press. 2002. Page 353.

16. Back-propagating Action Potential (bAP)
*Further reading: https://en.wikipedia.org/wiki/Neural_backpropagation
Induction of tLTP requires activation of the presynaptic
input milliseconds before the bAP in the postsynaptic
dendrite.

17. *Figure adopted from https://doi.org/10.3389/fnsyn.2011.00004
Spike-Timing-Dependent Plasticity
(STDP)

18. Experiment Evidence of STDP
 From Wikipedia:
“Henry Markram, when he was in Bert Sakmann's lab and published their
work in 1997, used dual patch clamping techniques to repetitively
activate pre-synaptic neurons 10 milliseconds before activating the post-
synaptic target neurons, and found the strength of the synapse
increased. When the activation order was reversed so that the pre-
synaptic neuron was activated 10 milliseconds after its post-synaptic
target neuron, the strength of the pre-to-post synaptic connection
decreased.
Further work, by Guoqiang Bi, Li Zhang, and Huizhong Tao in Mu-Ming
Poo's lab in 1998, continued the mapping of the entire time course
relating pre- and post-synaptic activity and synaptic change, to show that
in their preparation synapses that are activated within 5-20 ms before a
postsynaptic spike are strengthened, and those that are activated within a
similar time window after the spike are weakened.”
*Further reading: https://en.wikipedia.org/wiki/Spike-timing-dependent_plasticity

19. Cortical Column
*Figure adopted from https://bit.ly/2OZQpKA

20. Lateral Inhibition
Lateral inhibition is a Central Nervous System process whereby
application of a stimulus to the center of the receptive field excites a
neuron, but a stimulus applied near the edge inhibits it.
*Figure adopted from https://bit.ly/2yaat37

21. Lateral Inhibition
(Cont’d)
*Figure adopted from http://wei-space.blogspot.tw/2007/11/lateral-inhibition.html
& https://en.wikipedia.org/wiki/Lateral_inhibition

22. Hierarchical Sparse Distributed
Representations in Visual Cortex
*Figure adopted from https://bit.ly/2Ov5qV2 & https://bit.ly/2xTS1fw

23. Dopamine: Essential for Reward
Processing in Mammalian Brain
*Figure adopted from http://www.jneurosci.org/content/29/2/444
Dopamine neurons form huge synaptic contacts to target!

24. Learning Rule

25. Two Hot Approaches
 Supervised: Stochastic Gradient Descent
based Backpropagation learning rule
(Treat the membrane potentials of spiking neurons as
differentiable signals, where discontinuities at spike
times are considered as noise.*)
Unsupervised: STDP (Spike-Timing-
Dependent Plasticity) based learning rule
*Refer to Jun Haeng Lee, et al., Training Deep Spiking Neural Networks Using Backpropagation.
Frontiers in Neuroscience, 08 November 2016. https://doi.org/10.3389/fnins.2016.00508

26. *Refer to Yu, Q., Tang, H., Hu, J., Tan, K.C., Neuromorphic Cognitive Systems: A Learning and Memory
Centered Approach. Springer International Publishing. 2017. Page 9.
STDP Learning Rule

27. STDP Learning Rule (1-to-1)
*Figure adopted from http://dx.doi.org/10.7551/978-0-262-33027-5-ch037

28. STDP Learning Rule (2-to-1)
N0 is stimulated until N1 fires, then e0 is stopped for 30 ms.
N2 is stimulated by e2 during those 30 ms.
*Figure adopted from http://dx.doi.org/10.7551/978-0-262-33027-5-ch037

29. STDP Finds Spike Patterns
*Figure adopted from https://doi.org/10.1371/journal.pone.0001377

30. Triplet STDP
*Figure adopted from https://doi.org/10.1523/JNEUROSCI.1425-06.2006

31. Triplet STDP with traces
*Figure adopted from https://doi.org/10.1007/s00422-008-0233-1

32. Reward-modulated STDP
*Figure adopted from https://doi.org/10.1371/journal.pcbi.1000180

33. Neural Modeling

34. 1st Generation of Neuron Models
(McCulloch–Pitts Neuron Model)
*Figure adopted from http://wwwold.ece.utep.edu/research/webfuzzy/docs/kk-thesis/kk-thesis-html/node12.html

35. 2nd Generation of Neuron Models
*Figure adopted from http://cs231n.github.io/neural-networks-1/

36. 3rd Generation of Neuron Models
(Spiking Neuron Models)
*Figure adopted from http://kzyjc.cnjournals.com/html/2018/5/20180512.htm

37. Spiking Neuron Models
Miscellaneous models (integrators / resonators):
 Hodgkin-Huxley model
 Izhikevish model
 Leaky Integrate-and-Fire (LIF) model
 Resonate-and-Fire model
 Spike Response model (SRM)
……
*Further reading: https://en.wikipedia.org/wiki/Biological_neuron_model
& http://www.scholarpedia.org/article/Spike-response_model

38. Hodgkin-Huxley Model
*Figure adopted from Wulfram Gerstner & Werner M. Kistler. Spiking Neuron Models: Single Neurons,
Populations, Plasticity. Cambridge University Press. 2002. Page 34.

39. Hodgkin-Huxley Model (Cont’d)
*Taken from: https://www.bonaccorso.eu/2017/08/19/hodgkin-huxley-spiking-neuron-model-python/amp/

40. Izhikevich Model
*Taken from: http://www.physics.usyd.edu.au/teach_res/mp/ns/doc/nsIzhikevich3.htm

41. Izhikevich Model (Cont’d)
*Refer to Simple Model of Spiking Neurons (2003) https://www.izhikevich.org/publications/spikes.htm

42. Leaky Integrate-and-Fire Model
*Figure adopted from Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski “Neuronal Dynamics:
From Single Neurons to Networks and Models of Cognition” Cambridge University Press. 2014. Page 11.

43. The Firing of a Leaky Integrate-and-
Fire Model Neuron
*Figure adopted from https://doi.org/10.1371/journal.pone.0001377

44. Resonate-and-Fire Model
*Refer to Resonate-and-Fire Neurons (2001) https://www.izhikevich.org/publications/resfire.htm

45. Neural Coding

46. Hypothesized Neural Coding Schemes
 Rate Coding
 Temporal Coding
 Population Coding
 Sparse Coding
*Further reading: https://en.wikipedia.org/wiki/Neural_coding

47. Rate Coding
*Further reading: http://lcn.epfl.ch/~gerstner/SPNM/node7.html
Rate as a Spike Density
Rate as a Population Activity

48. Temporal Coding
*Further reading: http://lcn.epfl.ch/~gerstner/SPNM/node8.html
Time-to-First-Spike
(Latency Code)
Firing at Phases respecting
to Oscillation
Interspike synchrony

49. Population Coding
*Figure adopted from https://doi.org/10.1038/35039062

50. Sparse Coding
*Figure adopted from http://brainworkshow.sparsey.com/measuring-similarity-in-localist-vs-distributed-representations/

51. Sparse Coding with Inhibitory Neurons
 Population sparseness: Few neurons are
active at any given time
 Lifetime sparseness: Individual neurons
are responsive to few specific stimuli
*Figure adopted from https://doi.org/10.1523/JNEUROSCI.4188-12.2013

52. Neuromorphic
Computing

53. Categories of AI Chips
 AI Accelerator
 GPU
 FPGA
 ASIC
 Neuromorphic chip
 Network-on-Chip
 Memory-based
 Memristor-based
 Many-core CPU
 DSP
 Spintronics-based
 Photonics-based

54. Why Neuromorphic
*Figure adopted from https://bit.ly/31v1NAS

55. IBM’s TrueNorth Chip
*Figure adopted from https://doi.org/10.1126/science.1254642
*Video demo https://youtu.be/7ELRZrjCFd0

56. Intel’s Loihi Chip
*Figure adopted from https://doi.org/10.1109/MM.2018.112130359
*Video demo https://youtu.be/cDKnt9ldXv0

57. BrainChip’s Akida NSoC
*Figure adopted from https://www.brainchipinc.com/products/akida-neuromorphic-system-on-chip
*Video demo https://bit.ly/35rea45

58. 北京清華大學「天機芯」
*Video demo https://youtu.be/Nf0qVjT9WV0
*Figure adopted from https://doi.org/10.1038/s41586-019-1424-8

59. ANN-to-SNN Conversion
 Train ANNs using standard supervised training
techniques like backpropagation to leverage
the superior performance of trained ANNs and
subsequently convert to event-driven SNNs for
inference operation on neuromorphic platform.
 Rate-encoded spikes are approximately
proportional to the magnitude of the original
ANN inputs.

60. ANN-to-SNN Conversion
(Cont’d)
*Figure adopted from https://arxiv.org/abs/1802.02627
A Poisson event-generation process is used to produce the input spike
train to the network.

61. Software Simulation
 MATLAB
 PyNN http://neuralensemble.org/PyNN/
 BindsNET (with PyTorch)
https://github.com/Hananel-Hazan/bindsnet
 Brian http://briansimulator.org/
 Nengo https://www.nengo.ai/
 NEST http://www.nest-simulator.org/

62. Further Reading
 Wulfram Gerstner & Werner M. Kistler, “Spiking Neuron Models:
Single Neurons, Populations, Plasticity”. Cambridge University
Press (2002)
 Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam
Paninski, “Neuronal Dynamics: From Single Neurons to Networks
and Models of Cognition”. Cambridge University Press (2014)
 Eugene M. Izhikevich, “The Dynamical Systems in Neuroscience:
Geometry of Excitability and Bursting”. The MIT Press (2007)
 Nikola K. Kasabov, “Time-Space, Spiking Neural Networks and
Brain-Inspired Artificial Intelligence”. Springer International
Publishing (2018)
 蔺想红、王向文, “脉冲神经网络原理及应用”. 科学出版社 (2018)