Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks 12

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1 Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks 12 André Grüning, Brian Gardner and Ioana Sporea Department of Computer Science University of Surrey Guildford, UK 9th November $Id: multilayerspiker.txt :39:53Z ag15 $

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4 1 Introduction 2 Background 3 Our Approach 4 Results 5 Summary

5 What are we doing? What are we doing? Formulate a supervised learning rule for spiking neural networks that Why worthwhile? can train spiking networks containing a hidden layer of neurons, can map arbitrary spatio-temporal input into arbitrary output spike patterns, ie multiple spike trains. Understand how spike-pattern based information processing takes place in the brain. A learning rule for spiking neural networks with technical potential. Find a rule that is to spiking networks what is backprop to rate neuron networks. Human Brain Project

6 Scientific Area Where are we scientifically? In the middle of nowhere between: computational neuroscience cognitive science artificial intelligence / machine learning

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8 1 Introduction 2 Background 3 Our Approach 4 Results 5 Summary

9 Spiking Neurons (a) input spikes output spike u (c) output spike (b) input spikes Spiking neurons: real neurons communicate with each other via sequences of pulses spikes. 1 Dendritic tree, axon and cell body of a neuron. 2 Top: Spikes arrive from other neurons and its membrane potential rises. Bottom: incoming spikes on various dendrites elicit timed spikes responses as the output. 3 response of the membrane potential to incoming spikes. If the threshold θ is crossed, the membrane potential is reset to a low value, and a spike fired. From Andre Gruning and Sander Bohte. Spiking neural networks: Principles and challenges. In Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning ESANN, Brugge, 214. Invited Contribution.

10 Spiking Neurons Spiking Information Processing The precise timing of spikes generated by neurons conveys meaningful information. Synaptic plasticity forms the basis of learning. Changes in synaptic strength depend on relative pre- and postsynaptic spike times, and third signals. Challenge: to relate such localised plasticity changes to learning on the network level.

11 Learning for Spiking NN General Learning Algorithms for Spiking NN? There is no general-purpose algorithm for spiking neural networks. Challenge: discontinuous nature of spiking events. Various supervised learning algorithms exist, each with its own limitations eg: network topology, adaptability (e.g. reservoir computing), limited spike encoding (e.g. latency, or spike vs no spike). Most focus on classification rather than more challenging tasks like mapping from one spike train to another.

12 Some Learning Algorithms for Spiking NN SpikeProp 3, ReSuMe 4, Tempotron 5, Chronotron 6, SPAN 7, Urbanczik and Senn 8, Brea et al. 9, Freimaux et al. 1,... 3 S.M. Bohte, J.N. Kok, and H. La Poutré. Spike-prop: error-backpropagation in multi-layer networks of spiking neurons. Neurocomputing, 48(1 4):17 37, 22 4 Filip Ponulak and Andrzej Kasiński. Supervised learning in spiking neural networks with ReSuMe: Sequence learning, classification and spike shifting. Neural Computation, 22:467 51, 21 5 Robert Gütig and Haim Sompolinsky. The tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience, 9(3), 26. doi: 1.138/nn Răzvan V Florian. The chronotron: A neuron that learns to fire temporally precise spike patterns. PLoS ONE, 7(8):e4233, A. Mohemmed, S. Schliebs, and N. Kasabov. SPAN: Spike pattern association neuron for learning spatio-temporal sequences. Int. J. Neural Systems, R. Urbanczik and W. Senn. A gradient learning rule for the tempotron. Neural Computation, 21:34 352, 29 9 Johanni Brea, Walter Senn, and Jean-Pascal Pfister. Matching recall and storage in sequence learning with spiking neural networks. The Journal of Neuroscience, 33(23): , Nicolas Fremaux, Henning Sprekeler, and Wulfram Gerstner. Functional requirements for reward-modulated spile-timing-dependent plasticity. The Journal of Neuroscience, 3(4): , 1 21

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14 1 Introduction 2 Background 3 Our Approach 4 Results 5 Summary

15 Our Approach MultilayerSpiker Generalise backpropagation to Spiking Neural Networks with hidden neurons. Use stochastic neuron model to connect smooth quantities (derivative exists) with discrete spike trains (no derivative)

16 Neuron model Membrane potential u o (t) := h w oh t t Y h (t )ɛ(t t )dt + Z o (t )κ(t t )dt, (1) o postsynaptic neurons, h presynaptic neuron u o membrane potential of o. w oh strength of synaptic connection from h to o. Y h (t) = t h <t δ(t t h) spike train of neuron h where t h are the firing times of h Z o (t) = t o<t δ(t t o) spike train of neuron o where t o are the firing times of o.

17 Neuron model Spike response kernel ɛ and reset kernel κ ɛ(s) = ɛ [e s/τm e s/τs ] Θ(s) and κ(s) = κ e s/τm Θ(s), (2) spike response kernel ɛ = 4mV, reset kernel κ = 15mV, membrane time constant τ m = 1ms, the synaptic rise time τ s = 5ms Heaviside step function Θ(s).

18 Neuron model Stochastic Intensity (instantaneous firing rate) and Spikes ( ) u(t) ϑ ρ(t) = ρ[u(t)] = ρ exp, (3) u firing rate at threshold ρ =.1ms 1. threshold ϑ = 15mV. smoothness of the threshold u o =.2mV (output layer) or u h = 2mV (hidden layer) Spikes are generated by a point process taking stochastic intensity ρ o (t). Ie in a small time interval [t, t + δt) a spike is generated with probability ρ o (t)δt.

19 Backpropagation Objective ( Error ) function ( ) P(zo ref x) = exp log (ρ o (t)) Zo ref (t) ρ o (t)dt, (4) where Zo ref (t) = f δ(t t o f ) is the target output spike train for input x. a a J. P. Pfister, T. Toyoizumi, K. Aihara, and W. Gerstner. Optimal spike-timing dependent plasticity for precise action potential firing in supervised learning. Neural Computation, 18(6): , 26 Backprop approach w oh = η o log P(z ref x) w oh (5)

20 Backprop approach... and some ten slides later Lots of derivatives, indices, probabilities. Derivatives only possible due to smoothness of probability function. Relatively freely switching between expected values and their best estimates to be had when you only have single cast.

21 Backprop Weight Update Backpropagated Error Signal δ o (t) := 1 [ u o Hidden-to-Output Weights Input-to-Hidden Weights a Zo ref ] (t) ρ o (t), (6) T w oh = η o δ o (t) (Y h ɛ)(t) dt. (7) w hi = η T h w oh δ o (t)([y h (X i ɛ)] ɛ)(t)dt. (8) u h o a Brian Gardner, Ioana Sporea, and Andre Gruning. Learning spatio-temporally encoded pattern transformations in structured spiking neural networks. Neural Computation, To appear Preprint available at

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23 1 Introduction 2 Background 3 Our Approach 4 Results 5 Summary

24 Task Task Purpose: explore the properties of the new learning algorithm. Map an input (given as a set of spike trains) to an output (given again as a set of spike trains). Simulation details a. a Brian Gardner, Ioana Sporea, and Andre Gruning. Learning spatio-temporally encoded pattern transformations in structured spiking neural networks. Neural Computation, To appear Preprint available at

25 Introduction Background Our Approach Results Network Setup Input spike pattern 1 Input A Xi Hidden D Hidden neuron B Episodes Output 4 2 Output neuron E Time (ms) 4 5 Distance C Episodes Episodes 8 1 Left: spike rasters of input, hidden and output layers (with targets). Right top: network structure, bottom: van-rossum distance. Summary

26 Network in Action X i u h ϑ u o ϑ T (X i ǫ) T ([Y h (X i ǫ)] ǫ) T w hi Left: Input spike train X i (top) and its evoked post synaptic potential X i ɛ (bottom). Middle: Fluctuations of a hidden neuron membrane potential u h relative to a firing threshold ϑ, in response to inputs from input layer (top). The potential dependent factor of the back propagated error from hidden to input layer [Y h (X i ɛ)] bottom left: corresponding PSP (according to kernel X i ɛ). Right: membrane potential of an output neuron u o, in response to hidden layer activity. Target indicated by dotted lines (top). Weight changes of input-to-hidden weight due to learning rule.

27 Experiments Performance (%) Input patterns Episodes Free w hi Fixed w hi Single layer Input patterns Dependence of the performance on the number of input patterns and network setup. Each input pattern mapped to a unique target output of single output neuron and spike. Left: performance as a function of the number of input patterns. Right: Number of episodes to convergence in learning. Blue curves: hidden weights w hi updated according to learning algorithm, red curves: fixed random weights (plus homoeostasis), green: single layer.

28 Experiments A Performance (%) n o = 1 n o = 2 n o = n h / n o B n h / n o n o = Number of output spikes Dependence of the performance on the ratio of hidden to output neurons, and the number of target output spikes. p = 5. A unique target output spike pattern for each output neuron. (Left) Performance as a function of the ratio of hidden to output neurons. (Right) Minimum ratio of hidden to output neurons required to achieve 9% performance.

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30 1 Introduction 2 Background 3 Our Approach 4 Results 5 Summary

31 Summary Results Compared to other learning algorithms for spiking neuron networks, we can learn more input-output mappings: 2 classes or 2 individual patterns here vs 3-4 more timed output spikes: up to 1 individually timed spikes here vs 3-5 with multiple outputs: up to 3 here vs 1 Apply it! MultilayerSpiker opens up the use of spiking neural networks for technical/cognitive modelling tasks. Spiking networks are biologically plausible. Explore how computations can be done with neural networks. Next step in the Human Brain Project: Implementation on SpiNNaker, and other neural hardware.

32 Spiking Neural Networks Open Questions How do networks of spiking neurons carry out computations? How can they learn such computations? Does this explain how real biological neurons compute? What is the applied killer application?