- spiking neural networks
- spiking neuron models
- spike timing-dependent plasticity
- neuron encoding
- co-ordinate transformation.
Learning Mechanisms in Networks of Spiking Neurons. / Wu, Qingxiang; McGinnity, TM; Maguire, LP; Glackin, Brendan; Belatreche, Ammar.Studies in Computational Intelligence. ed. / Ke Chen; Lipo Wang. Vol. 35 Springer, 2007. p. 171-197.
Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Learning Mechanisms in Networks of Spiking Neurons
AU - Wu, Qingxiang
AU - McGinnity, TM
AU - Maguire, LP
AU - Glackin, Brendan
AU - Belatreche, Ammar
N1 - Reference text: 1. Maass, W., Schnitger, G., and Songtag, E.: On the computational power of sigmoid versus Boolean threshold circuits. Proc. Of the 32nd Annual IEEE Symposium on Foundations of Computer Science. (1991)767-776 2. Maass, W.: Networks of spiking neurons: The third generation of neural network models. Neural Networks. 10(9): (1997)1659—1671 3. Hodgkin, A. and Huxley, A.: A quantitative description of membrane current and its appli-cation to conduction and excitation in nerve. J. Physiol. (London) Vol. 117, (1952)500-544 4. Gerstner and Kistler: Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge University Press, (2002) 5. Melamed, O., Gerstner, W., Maass, W., Tsodyks, M. and Markram, H.: Coding and Learning of behavioral sequences, Trends in Neurosciences, Vol.27 (2004)11-14 6. Theunissen, F.E. and Miller, J.P.: Temporal Encoding in Nervous Systems: A Rigorous Definition. J. Comp. Neurosci. (1995)2:149-162 7. Bohte, S.M., Kok, J.N. and Poutré, H.La.: SpikeProp: Error-Backpropagation for Networks of Spiking Neurons. Neurocomputing. 48(1-4) (2002)17-37 8. Wu, Q.X., McGinnity, T.M., Maguire L.P., Glackin, B. and Belatreche, A.: Supervised Training of Spiking Neural Networks With Weight Limitation Constraints. Proceedings of International conference on Brain Inspired Cognitive Systems. University of Stirling, Scot-land, UK, (2004) 9. Sohn, J.W., Zhang, B.T., and Kaang, B.K.: Temporal Pattern Recognition Using a Spiking Neural Network with Delays. Proceedings of the International Joint Conference on Neural Networks (IJCNN'99). vol. 4 (1999)2590-2593, 10. Mykola Lysetskiy, Andrsej Ozowski and Jacek M.Zurada: Invariant Recognition of Spatio-Temporal Patterns in The Olfactory System Model, Neural Processing Letters 15:225 –234, Kluwer Academic Publishers. Printed in the Netherlands, 2002 11. Choe, Y. and Miikulainen, R.: Self-organization and segmentation in a laterally connected orientation map of spik-ing neurons. Neurocomputing. 21(1998)139-157 12. Sirosh, J., Miikkulainen, R.,: Topographic receptive fields and patterned lateral interaction in a selforganizing model of the primary visual cortex. Neural Computation. 9 (1997) 577-594 13. Bi, G.Q., Poo, M.M.: Distributed synaptic modification in neural networks induced by pat-terned stimulation. Nature 401 (1999)792 - 796 14. Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18 (1998)10464–10472 15. Bell, C.C., Han, V.Z., Sugavara, Y., Grant, K.: Synaptic plasticity in the mormyrid elec-trosensory lobe. J. Exp. Biol. 202(1999)1339–1347 16. Rossum, M.C.W., Bi, G.Q., and Turrigiano, G.G.: Stable Hebbian Learning from Spike Timing-Dependent Plasticity. The Journal of Neuroscience. 20(23)(2000)8812–8821 17. Neuron Software download website: http://neuron.duke.edu/. 18. Wu, Q.X., McGinnity, T.M., Maguire, L.P., Glackin, B. and Belatreche, A.: Learning under weight constraints in networks of temporal encoding spiking neurons. International Journal of NEUROCOMPUTING. Special issue on Brain Inspired Cognitive Systems. (2006) in press. 19. Müller, E.: Simulation of High-Conductance States in Cortical Neural Networks. Masters thesis, University of Heidelberg, HD-KIP-03-22, (2003) 20. Christof Koch: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, (1999). 21. Peter Dayan and Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, Cambridge, Massachusetts, (2001). 22. Wulfram Gerstner and Werner Kistler: Spiking Neuron Models: Single Neurons, pula-tions,Plasticity. Cambridge University Press, (2002). 23. SenseMaker Project (IST–2001-34712) funded by the European Union under the “Information Society Technologies” Programme (2002-2006). 24. Theunissen, F.E. and Miller, J.P.: Temporal Encoding in Nervous Systems: A Rigorous Definition. J. Comp. Neurosci., 2 (1995)149-160 26. Song, S., Miller, K.D., and Abbott, L.F.: Competitive Hebbian learning though spike-timing dependent synaptic plasticity. Nature Neuroscince, 3 (2000) 919-926 27. Song, S. and Abbott, L.F.: Column and Map Development and Cortical Re-Mapping Through Spike-Timing Dependent Plasticity. Neuron 32 (2001) 339-350 28. Deneve S., Latham P. E. and Pouget A.: Efficient computation and cue integration with noisy population codes, Nature Neuroscience, 4 (2001) 826-831 29. Marisa Taylor-Clarke, Steffan Kennett, Patrick Haggard: Persistence of visual-tactile en-hancement in humans. Neuroscience Letters. Vol. 354, No.1, Elsevier Science Ltd, (2004) 22–25 30. Joseph E. Atkins, Robert A. Jacobs, and David C. Knill: Experience-dependent visual cue recalibration based on discrepancies between visual and haptic percepts. Vision Research, Vol.43(25) (2003) 2603-2613 31. Spence, C., Pavani, F., Driver, J.: Crossmodal links between vision and touch in covert endogenous spatial attention. J. Exp. Psychol. Hum. Percept. Perform. 26 (2000) 1298–1319 32. Eimer M., Driver, J.: An event-related brain potential study of crossmodal links in spatial attention between vision and touch, Psychophysiology, 37 (2000) 697–705 33. Graziano, M.S.A., Gross, C.G.: The representation of extrapersonal space: A possible role for bimodal, visual–tactile neurons, in: M.S. Gazzaniga (Ed.), The Cognitive Neurosciences, MIT Press, Cambridge, MA, (1994) 1021–1034 34. Zhou, Y.D., Fuster, J.M.: Visuo-tactile cross-modal associations in cortical somatosensory cells. Proc. Natl. Acad. Sci. USA 97 (2000) 9777–9782 35. Meftah, E.M., Shenasa, J.: Chapman, C.E., Effects of a cross-modal manipulation of attention on somatosensory cortical neuronal responses to tactile stimuli in the monkey. J. Neu-rophysiol. 88 (2002) 3133–3149 36. Kennett, S., Taylor-Clarke, M., Haggard, P.: Noninformative vision improves the spatial resolution of touch in humans. Curr. Biol. 11 (2001) 1188–1191 37. Johansson, R.S., Westling, G.: Signals in tactile afferents from the fingers eliciting adaptive motor-responses during precision grip. Exp. Brain. Res. 66 (1987) 141–154 38. Galati, Gaspare-Committeri, Giorgia - Sanes, Jerome N. - Pizzamiglio, Luigi: Spatial coding of visual and somatic sensory information in body-centred coordinates. European Journal of Neuroscience. Vol.14, No.4, Blackwell Publishing, (2001) 737-748 39. Colby, C.L. & Goldberg, M.E.: Space and attention in parietal cortex. Annu. Rev. Neurosci., 22 (1999) 319-349 40. Gross, C.G., Graziano, M.S.A.: Multiple representations of space in the brain. Neuroscien-tist, 1 (1995) 43-50 41. Rizzolatti, G., Fogassi, L.& Gallese, V.: Parietal cortex: from sight to action. Curr. Opin. Neurobiol., 7 (1997) 562-567 42. Taylor-Clarke M., Kennett S., Haggard P.: Vision modulates somatosensory cortical pro-cessing. Curr. Biol. 12 (2002) 233–236 43. Iriki, A., Tanaka, M., Iwamura, Y.: Attention-induced neuronal activity in the monkey somatosensory cortex revealed by pupillometrics. Neurosci. Res. 25 (1996) 173–181 44. Simon Thorpe, Arnaud Delorme and Rufin Van Rullen: Spike-based strategies for rapid processing. Neural Networks, Vol.14, Issue 6-7 (2001)715-725 45. Wu, Q.X., McGinnity, T.M., Maguire, L.P., Belatreche, A. and Glackin, B.: Adaptive Co-Ordinate Transformation Based on Spike Timing-Dependent Plasticity Learning Paradigm. Proceedings of The First International Conference on Natural Computation, LNCS, Vol.3610 (2005)420-429
PY - 2007/1/1
Y1 - 2007/1/1
N2 - In spiking neural networks, signals are transferred by action potentials. The information is encoded in the patterns of neuron activities or spikes. These features create significant differences between spiking neural networks and classical neural networks. Since spiking neural networks are based on spiking neuron models that are very close to the biological neuron model, many of the principles found in biological neuroscience can be used in the networks. In this chapter, a number of learning mechanisms for spiking neural networks are introduced. The learning mechanisms can be applied to explain the behaviours of networks in the brain, and also can be applied to artificial intelligent systems to process complex information represented by biological stimuli.
AB - In spiking neural networks, signals are transferred by action potentials. The information is encoded in the patterns of neuron activities or spikes. These features create significant differences between spiking neural networks and classical neural networks. Since spiking neural networks are based on spiking neuron models that are very close to the biological neuron model, many of the principles found in biological neuroscience can be used in the networks. In this chapter, a number of learning mechanisms for spiking neural networks are introduced. The learning mechanisms can be applied to explain the behaviours of networks in the brain, and also can be applied to artificial intelligent systems to process complex information represented by biological stimuli.
KW - spiking neural networks
KW - learning
KW - spiking neuron models
KW - spike timing-dependent plasticity
KW - neuron encoding
KW - co-ordinate transformation.
UR - http://www.springerlink.com/content/n605v2m520478859/
UR - http://www.springerlink.com/content/n605v2m520478859/
U2 - 10.1007/978-3-540-36122-0_7
DO - 10.1007/978-3-540-36122-0_7
M3 - Chapter
SN - 1860-949X
VL - 35
SP - 171
EP - 197
BT - Studies in Computational Intelligence
A2 - Chen, Ke
A2 - Wang, Lipo
PB - Springer