DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

[1]  Jing Yang,et al.  A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks , 2013, Neural Networks.

[2]  Henrik Jörntell,et al.  Synaptic Memories Upside Down: Bidirectional Plasticity at Cerebellar Parallel Fiber-Purkinje Cell Synapses , 2006, Neuron.

[3]  Dominique Debanne,et al.  Presynaptic action potential waveform determines cortical synaptic latency , 2011, The Journal of physiology.

[4]  Nikola Kasabov,et al.  Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. , 2013, Neural networks : the official journal of the International Neural Network Society.

[5]  Sander M. Bohte,et al.  The evidence for neural information processing with precise spike-times: A survey , 2004, Natural Computing.

[6]  AdeliHojjat,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007 .

[7]  Michael Schmitt,et al.  Learning Temporally Encoded Patterns in Networks of Spiking Neurons , 2004, Neural Processing Letters.

[8]  Nicholas T. Carnevale,et al.  Simulation of networks of spiking neurons: A review of tools and strategies , 2006, Journal of Computational Neuroscience.

[9]  Ammar Belatreche,et al.  A Method for Supervised Training of Spiking Neural Networks , 2003 .

[10]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[11]  Jean-Pascal Pfister,et al.  Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning , 2005, Neural Computation.

[12]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[13]  Stefan Schliebs,et al.  Training spiking neural networks to associate spatio-temporal input-output spike patterns , 2013, Neurocomputing.

[14]  Linda Bushnell,et al.  Fast Modifications of the SpikeProp Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[15]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[16]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[17]  Haizhou Li,et al.  A Spike-Timing-Based Integrated Model for Pattern Recognition , 2013, Neural Computation.

[18]  Yubing Gong,et al.  Delay-induced firing behavior and transitions in adaptive neuronal networks with two types of synapses , 2013, Science China Chemistry.

[19]  Stefan Schliebs,et al.  Span: Spike Pattern Association Neuron for Learning Spatio-Temporal Spike Patterns , 2012, Int. J. Neural Syst..

[20]  Mohammad Reza Meybodi,et al.  Unsupervised learning of synaptic delays based on learning automata in an RBF-like network of spiking neurons for data clustering , 2005, Neurocomputing.

[21]  Cornelius Glackin,et al.  Receptive field optimisation and supervision of a fuzzy spiking neural network , 2011, Neural Networks.

[22]  Razvan V. Florian,et al.  The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns , 2010, PloS one.

[23]  Wolfgang Maass,et al.  Computing and learning with dynamic synapses , 1999 .

[24]  Duc Truong Pham,et al.  Control chart pattern clustering using a new self-organizing spiking neural network , 2008 .

[25]  Paul H. E. Tiesinga,et al.  A New Correlation-Based Measure of Spike Timing Reliability , 2002, Neurocomputing.

[26]  Robert A. Legenstein,et al.  What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? , 2005, Neural Computation.

[27]  Matthieu Gilson,et al.  Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity , 2012, Neural Computation.

[28]  R. Angus Silver,et al.  Estimation of the time course of neurotransmitter release at central synapses from the first latency of postsynaptic currents , 2012, Journal of Neuroscience Methods.

[29]  J. J. Hopfield,et al.  Pattern recognition computation using action potential timing for stimulus representation , 1995, Nature.

[30]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[31]  Peter A. Cariani,et al.  Temporal codes and computations for sensory representation and scene analysis , 2004, IEEE Transactions on Neural Networks.

[32]  B. Katz,et al.  The measurement of synaptic delay, and the time course of acetylcholine release at the neuromuscular junction , 1965, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[33]  Filip Ponulak,et al.  Introduction to spiking neural networks: Information processing, learning and applications. , 2011, Acta neurobiologiae experimentalis.

[34]  Eugene M. Izhikevich,et al.  Polychronization: Computation with Spikes , 2006, Neural Computation.

[35]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[36]  Jen-Wei Lin,et al.  Modulation of synaptic delay during synaptic plasticity , 2002, Trends in Neurosciences.

[37]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[38]  F. Ponulak.  ReSuMe-New Supervised Learning Method for Spiking Neural Networks , 2005 .

[39]  Hanna Parnas,et al.  Control of neurotransmitter release: From Ca2+ to voltage dependent G-protein coupled receptors , 2010, Pflügers Archiv - European Journal of Physiology.

[40]  Andrzej Kasiński,et al.  Comparison of supervised learning methods for spike time coding in spiking neural networks , 2006 .

[41]  Ioana Sporea,et al.  Supervised Learning in Multilayer Spiking Neural Networks , 2012, Neural Computation.

[42]  J Gautrais,et al.  Rate coding versus temporal order coding: a theoretical approach. , 1998, Bio Systems.

[43]  Julie A. Wall,et al.  A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization , 2010, Frontiers in computational neuroscience.

[44]  Andrzej J. Kasinski,et al.  Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.

[45]  Wulfram Gerstner,et al.  Phenomenological models of synaptic plasticity based on spike timing , 2008, Biological Cybernetics.