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Clustering using Spiking Neural Networks

Clustering using Spiking Neural Networks. Biological Neuron: The Elementary Processing Unit of the Brain. Biological Neuron: A Generic Structure. Axon. Axon Terminal. Synapse. Soma. Dendrite. Biological Neuron: Nerve Impulse Transiting. Membrane Potential. Action Potential (Spike).

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Clustering using Spiking Neural Networks

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  1. Clustering using Spiking Neural Networks

  2. Biological Neuron:The Elementary Processing Unit of the Brain

  3. Biological Neuron:A Generic Structure Axon Axon Terminal Synapse Soma Dendrite

  4. Biological Neuron:Nerve Impulse Transiting Membrane Potential Action Potential (Spike) Spike-After Potential Action Potential (Spike) Postsynaptic Potential

  5. Biological Neuron:Soma Firing Behavior Synchrony is the main factor of soma firing

  6. Biological Neuron:Information Coding Firing rate alone does not carry all the relevant information Neurons communicate via exact spike timing

  7. Neuroscience Models of Neuron:The Hodgkin-Huxley Model Alan Lloyd Hodgkin and Andrew Huxley received the Nobel Prize in Physiology and Medicine in 1963 The Hodgkin-Huxley model is too complicated model of neuron to be used in artificial neural networks

  8. Neuroscience Models of Neuron:Leaky Integrate-And-Fire Model or Leaky Integrate-And-Fire model disregards the refractory capability of neuron

  9. Neuroscience Models of Neuron:Spike-Response Model Spike-Response model captures the major elements of a biological neuron behavior

  10. Biological Neuron – Computational Intelligence Approach:The First Generation The first artificial neuron was proposed by W. McCulloch & W. Pitts in 1943

  11. Biological Neuron – Computational Intelligence Approach:The Second Generation Multilayered Perception is a universal approximator

  12. Biological Neuron – Computational Intelligence Approach:Artificial Neurons – Too Artificial? From neurophysiology point of view, y is existence of an output spike Number of spikes spike occurrence Time frame spike absence From neurophysiology point of view, y is firing rate Spike timing is not considered at all!

  13. Biological Neuron – Computational Intelligence Approach:The Third Generation Spiking neuron model was introduced by J. Hopfield in 1995 Spiking neural networks are - biologically more plausible, - computationally more powerful, - considerably faster than networks of the second generation

  14. Spiking Neural Network:Overall Architecture Spiking neural network is a heterogeneous two-layered feed-forward network with lateral connections in the second hidden layer RN is a receptive neuron MS is a multiple synapse SN is a spiking neuron

  15. Spiking Neural Network:Population Coding Input spike: Pool of Receptive Neurons

  16. Spiking Neural Network:Multiple Synapse Spike-response function: Delayed postsynaptic potential: Total postsynaptic potential: Membrane potential:

  17. Spiking Neural Network:Hebbian Learning – WTA and WTM Winner-Takes-All: Winner-Takes-More*: *Proposed for the first time on the 11th International Conference on Science and Technology “System Analysis and Information Technologies” (Kyiv, Ukraine, 2009) by Ye. Bodyanskiy and A. Dolotov

  18. Spiking Neural Network:Image Processing* Original Image SOM at 50 epoch SNN at 4 epoch *In Bionics of Intelligence: 2007, 2 (67), pp. 21-26 by Ye. Bodyanskiy and A. Dolotov

  19. Spiking Neuron:The Laplace Transform Basis From control theory point of view, action potential (spike) is a signal in pulse-position form: Thus, transformation of action potential to postsynaptic potential taken into synapse is nothing other than pulse-position – continuous-time transformation, and soma transformation is just reverse one, continuous-time – pulse-position transformation

  20. Spiking Neuron Synapse:A 2nd order critically damped response unit * *Proposed for the first time on the 6th International Conference “Information Research and Applications” (Varna, Bulgaria, 2009) by Ye. Bodyanskiy, A. Dolotov, and I. Pliss

  21. Spiking Neuron:Technically Plausible Description* Incoming Spike: Time Delay: Membrane Potential: Spike-Response Function: Relay: Outgoing Spike: *Proposed for the first time on the 6th International Conference “Information Research and Applications” (Varna, Bulgaria, 2009) by Ye. Bodyanskiy, A. Dolotov, and I. Pliss

  22. Spiking Neuron:Analog-Digital Architecture* Analog-digital spiking neurons corresponds to spike-response model entirely * Proposed for the first time in Image Processing / Ed. Yung-Sheng Chen: In-Teh, Vukovar, Croatia, pp. 357-380 by Ye. Bodyanskiy and A. Dolotov,

  23. Fuzzy Receptive Neurons*: Pool of receptive neurons is a linguistic variable, and a receptive neuron within a pool is a linguistic term. *Proposed for the first time in Information Technologies and Computer Engineering: 2009, 2(15), pp. 51-55 by Ye. Bodyanskiy and A. Dolotov

  24. Fuzzy Spiking Neural Network:Fuzzy Probabilistic Clustering* There is no need to calculate cluster centers! *Proposed for the first time in Sci. Proc. of Riga Technical University: 2008, 36, P. 27-33 by Ye. Bodyanskiy and A. Dolotov

  25. Fuzzy Spiking Neural Network:Fuzzy Possibilistic Clustering* *Proposed for the first time on the 15th Zittau East-West Fuzzy Colloquium (Zittau, Germany, 2008)by Ye. Bodyanskiy, A. Dolotov, I. Pliss, and Ye. Viktorov

  26. Fuzzy Spiking Neural Network:Image Processing* Original image Training set SOM at 40th epoch FSNN at 4th epoch *In Proceeding of the 4th International School-Seminar “Theory of Decision Making“ (Uzhhorod, Ukraine, 2008) by Ye. Bodyanskiy, A. Dolotov, and I. Pliss

  27. Fuzzy Spiking Neural Network:Image Processing* Original image Training set FCM at 29th epoch FSNN at 3rd epoch *In Proceeding of the 11th International Biennial Baltic Electronics Conference "BEC 2008“ (Tallinn/Laulasmaa, Estonia, 2008) by Ye. Bodyanskiy and A. Dolotov

  28. Fuzzy Spiking Neural Network:Image Processing* Original image Training set FSNN at 1st epoch FSNN at 3rd epoch FCM at 3rd epoch FCM at 30th epoch *In Image Processing / Ed. Yung-Sheng Chen: In-Teh, Vukovar, Croatia, pp. 357-380 by Ye. Bodyanskiy and A. Dolotov

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