Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition

Neural Networks

Volumn 41, Issue , 2013, Pages 188-201

  Kasabov, Nikola ^a,b,c   Dhoble, Kshitij ^a   Nuntalid, Nuttapod ^a   Indiveri, Giacomo ^b,c  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c ETH ZURICH   (Switzerland)

Author keywords

Dynamic synapses; EEG pattern recognition; Evolving connectionist systems; Moving object recognition; Rank order coding; Spatio temporal pattern recognition; Spike time based learning; Spiking neural networks

Indexed keywords

DYNAMIC SYNAPSIS; EEG PATTERN RECOGNITION; EVOLVING CONNECTIONIST SYSTEMS; MOVING OBJECT RECOGNITION; RANK-ORDER CODING; SPATIOTEMPORAL PATTERNS; SPIKING NEURAL NETWORKS; TIME BASED;

DIGITAL STORAGE; ELECTROENCEPHALOGRAPHY; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; OBJECT RECOGNITION; PLASTICITY; SPEECH RECOGNITION;

DYNAMIC MODELS;

ACCURACY; ALGORITHM; ARTICLE; BRAIN COMPUTER INTERFACE; LONG TERM DEPRESSION; LONG TERM POTENTIATION; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL NETWORK; NERVE CELL PLASTICITY; PATTERN RECOGNITION; PRIORITY JOURNAL; SPATIOTEMPORAL ANALYSIS; SPIKE WAVE;

ACTION POTENTIALS; ARTIFICIAL INTELLIGENCE; CONNECTOME; ELECTROENCEPHALOGRAPHY; HUMANS; MODELS, NEUROLOGICAL; MOTION PERCEPTION; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; PATTERN RECOGNITION, VISUAL; RECOGNITION (PSYCHOLOGY); TIME PERCEPTION;

EID: 84875878715     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.11.014     Document Type: Article

References (77)

1. Arthur J.V., Merolla P.A., Akopyan F., Alvarez R., Cassidy A., Chandra A., et al. Building block of a programmable neuromorphic substrate: a digital neurosynaptic core. Proc. IJCNN 2012 2012, IEEE Press, Brisbane.
2. Barker-Collo S., Feigin V.L., Parag V., Lawes C.M.M., Senior H. Auckland stroke outcomes study. Neurology 2010, 75(18):1608-1616.
3. Belatreche A., Maguire L.P., McGinnity M. Advances in design and application of spiking neural networks. Soft Computing 2006, 11(3):239-248.
4. Bellas F., Duro R.J., Faiña A., Souto D. MDB: artificial evolution in a cognitive architecture for real robots. IEEE Transactions on Autonomous Mental Development 2010, 2:340-354.
5. Benuskova L., Kasabov N. Computational neurogenetic modelling 2007, Springer, New York, p. 290.
6. Bichler O., Ouerlioz D., Thorpe S., Bourgoin J.-P., Gamrat C. Unsupervised features extraction from asynchronous silicon retina spike-timing-dependent plasticity. Proc. IJCNN 2011 2011, 859-866. IEEE Press.
7. Bohte S.M. The evidence for neural information processing with precise spike-times: a survey. Natural Computing 2004, 3.
8. Brader J., Senn W., Fusi S. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation 2007, 19(11):2881-2912.
9. Brette R., et al. Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience 2007, 23:349-398.
10. Defoin-Platel M., Schliebs S., Kasabov N. Quantum-inspired evolutionary algorithm: a multi-model EDA. IEEE Transactions on Evolutionary Computation 2009, 13(6):1218-1232. Dec.
11. Delbruck, T. (2007). jAER open source project. http://jaer.wiki.sourceforge.net.
12. Dhoble K., Nuntalid N., Indivery G., Kasabov N. On-line spatiotemporal pattern recognition with evolving spiking neural networks utilising address event representation, rank oder- and temporal spike learning. Proc. WCCI 2012 2012, 554-560. IEEE Press.
13. Ferreira A., Almeida C., Georgieva P., Tomé A., Silva F. Advances in EEG-based biometry. LNCS 2010, vol. 6112:287-295. Springer. A. Campilho, M. Kamel (Eds.).
14. Fusi S., Annunziato M., Badoni D., Salamon A., Amit Spike-driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Computation 2000, 12(10):2227-2258.
15. Gerstner W. Time structure of the activity of neural network models. Physical Review 1995, 51:738-758.
16. Guyonneau R., VanRullen R., Thorpe S. Neurons tune to the earliest spikes through STDP. Neural Computation 2005, 17(4):859-879. April 2005.
17. Hebb D. The organization of behavior 1949, John Wiley and Sons, New York.
18. Hodgkin A.L., Huxley A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 1952, 117:500-544.
19. Indiveri G., Chicca E., Douglas R.J. Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation 2009, 1(2):119-127.
20. Indiveri G., Horiuchi T. Frontiers in neuromorphic engineering. Frontiers in Neuroscience 2011, 5:118.
21. Indiveri G., et al. Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 2011, 5:1-23.
22. Indiveri, G., Stefanini, F., & Chicca, E. (2010). Spike-based learning with a generalized integrate and fire silicon neuron. In 2010 IEEE int. symp. circuits and syst., ISCAS 2010 (pp. 1951-1954). Paris, May 30-June 02.
23. Isa T., Fetz E.E., Muller K. Recent advances in brain-machine interfaces. Neural Networks 2009, 22(9):1201-1202. Brain-Machine Interface.
24. Izhikevich E.M. Which model to use for cortical spiking neurons?. IEEE Transactions on Neural Networks 2004, 15(5):1063-1070.
25. Izhikevich E.M. Polychronization: computation with spikes. Neural Computation 2006, 18:245-282.
26. Jin X., Lujan M., Plana L.A., Davies S., Temple S., Furber S. Modelling spiking neural networks on SpiNNaker. Computing in Science & Engineering 2010, 12(5):91-97. ISSN: 1521-961.
27. Kasabov N. Evolving fuzzy neural networks for on-line supervised/unsupervised, knowledge-based learning. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 2001, 31(6):902-918.
28. Kasabov N. Evolving connectionist systems: methods and applications in bioinformatics, brain study and intelligent machines 2002, Springer Verlag, London, New York, Heidelberg.
29. Kasabov N. Evolving connectionist systems - The knowledge engineering approach 2007, Springer.
30. Kasabov N. To spike or not to spike: a probabilistic spiking neuron model. Neural Networks 2010, 23(1):16-19.
31. Kasabov Evolving spiking neural networks and neurogenetic systems for spatio- and spectro-temporal data modelling and pattern recognition. LNCS 2012, vol. 7311:234-260. Springer-Verlag, Berlin, Heidelberg. J. Liu (Ed.).
32. Kasabov N. NeuCube EvoSpike architecture for spatio-temporal modelling and pattern recognition of brain signals. LNAI 2012, vol. 7477:225-243. Springer. Mana, Schwenker, Trentin (Eds.).
33. The springer handbook of bio- and neuroinformatics 2013, Springer. N. Kasabov (Ed.).
34. Kasabov N., Benuskova L., Wysoski S. A computational neurogenetic model of a spiking neuron. IJCNN 2005 conf. proc. vol. 1 2005, 446-451. IEEE Press.
35. Kasabov N., Hu Y. Integrated optimisation method for personalised modelling and case study applications. International Journal of Functional Informatics and Personalised Medicine 2010, 3(3):236-256.
36. Kasabov N., Schliebs R., Kojima H. Probabilistic computational neurogenetic framework: from modelling cognitive systems to alzheimer's disease. IEEE Transactions on Autonomous Mental Development 2011, 3(4):300-311.
37. Kasabov N., Song Q. DENFIS: dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction. IEEE Transactions on Fuzzy Systems 2002, 10:144-154.
38. Kistler G., Gerstner W. Spiking neuron models-single neurons, populations, plasticity 2002, Cambridge Univ. Press.
39. Lalor E., Kelly S., Finucane C., Burke R., Smith R., Reilly R., et al. Steady-state vep-based brain-computer interface control in an immersive 3D gaming environment. EURASIP Journal on Applied Signal Processing 2005, 2005:3156-3164.
40. Lichtsteiner P., Delbruck T. A 64×64 AER logarithmic temporal derivative silicon retina. Research in Microelectronics and Electronics 2005, 2:202-205.
41. Maass W., Markram H. Synapses as dynamic memory buffers. Neural Networks 2002, 15(2):155-161.
42. Maass W., Natschlaeger T., Markram H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation 2002, 14(11):2531-2560.
43. Maass W., Sontag E.D. Neural systems as nonlinear filters. Neural Computation 2000, 12(8):1743-1772.
44. Maass W., Zador A.M. Computing and learning with dynamic synapses. Pulsed neural networks 1999, 321-336. MIT Press.
45. Masquelier T., Guyonneau R., Thorpe S.J. Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 2008, 3(1):e1377.
46. Masquelier T., Guyonneau R., Thorpe S.J. Competitive STDP-based spike pattern learning. Neural Computation 2009, 21(5):1259-1276.
47. Mehrtash N., Jung D., Klar H. Image pre-processing with dynamic synapses. Neural Computing & Applications 2003, 12:33-41. 10.1007/s00521-030-0371-2.
48. Mitra S., Fusi S., Indiveri G. Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Transactions on Biomedical Circuits and Systems 2009, 3(1):32-42.
49. Moradi, S., & Indiveri, G. (2011). A VLSI network of spiking neurons with an asynchronous static random access memory. In Biomedical circuits and systems conference BIOCAS 2011.
50. Morrison A., Diesmann M., Gerstner W. Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics 2008, 98:459-478.
51. Namarvar, H., Liaw, J.-S., & Berger, T. (2001). A new dynamic synapse neural network for speech recognition. In Proc. IJCNN'01. int. joint conf. on neural networks, 2001.
52. Natschläger T., Maass W. Spiking neurons and the induction of finite state machines. Theoretical Computer Science-Natural Computing 2002, 287(1):251-265.
53. Neuper C., Muller G., Kubler A., Birbaumer N., Pfurtscheller G. Clinical application of an EEG -based brain-computer interface: a case study in a patient with severe motor impairment. Clinical Neurophysiology 2003, 114(3):399-409.
54. Nuntalid N., Dhoble K., Kasabov N. EEG classification with BSA spike encoding algorithm and evolving probabilistic spiking neural network. LNCS 2011, vol. 7062:451-460. Springer, Heidelberg.
55. Nuzly H., Kasabov N., Shamsuddin S. Probabilistic evolving spiking neural network optimization using dynamic quantum inspired particle swarm optimization. Australian Journal of Intelligent Information Processing Systems 2010, 11(1).
56. Schliebs S., Defoin-Platel M., Worner S., Kasabov N. Integrated feature and parameter optimization for evolving spiking neural networks: exploring heterogeneous probabilistic models. Neural Networks 2009, 22:623-632.
57. Schliebs S., Fiasch'e M., Kasabov N. Constructing robust liquid state machines to process highly variable data streams. LNCS 2012, vol. 7552:604-611. Springer. A. Vila (Ed.).
58. Schliebs S., Hamed H.N.A., Kasabov N. A reservoir-based evolving spiking neural network for on-line spatio-temporal pattern learning and recognition, Neural Information Processing. LNCS 2011, vol. 7063:160-168. Springer, Shanghai, China, Heidelberg.
59. Schliebs S., Kasabov N. Evolving spiking neural networks: a survey, evolving systems 2012, Springer.
60. Schliebs S., Kasabov N., Defoin-Platel M. On the probabilistic optimization of spiking neural networks. International Journal of Neural Systems 2010, 20(6):481-500.
61. Schrauwen B., Van Campenhout J. BSA-a fast and accurate spike train encoding scheme. 2003 Proc. int. joint conf. on neural networks. Vol. 4 2003, 2825-2830. IEEE.
62. Soltic S., Kasabov N. Knowledge extraction from evolving spiking neural networks with rank order population coding. International Journal of Neural Systems 2010, 20(6):437-445.
63. Sona D., Veeramachaneni S., Olivetti E., Avesani P. Inferring cognition from fMRI brain images. LNCS 2007, vol. 4669:869-878. Springer. J.M. de Sá, L.A. Alexandre, W. Duch, D.P. Mandic (Eds.).
64. Song S., Miller K., Abbott L., et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 2000, 3:919-926.
65. Tanaka K., Matsunaga K., Wang H. Electroencephalogram-based control of an electric wheelchair. IEEE Transactions on Robotics 2005, 21(4):762-766.
66. Thorpe S.J. The speed of categorization in the human visual system. Neuron 2009, 62(2):168-170.
67. Thorpe S.J. Spike-based image processing: can we reproduce biological vision in hardware. Lecture Notes in Computer Science 2012, vol. 7583:516-521.
68. Thorpe, S.J., Brilhault, A., & Perez-Carrasco, J.A. (2010). Suggestions for a biologically inspired spiking retina using order-based coding. In IEEE international symposium on circuits and syst. (pp. 265-268).
69. Thorpe S., Gautrais J. Rank order coding. Computational Neuroscience 1998, 13:113-119.
70. Thorpe S., Guyonneau R., Guilbaud N., Allegraud J.M., Vanrullen R. SpikeNet: real-time visual processing with one spike per neuron. Neurocomputing 2004, 58-60:857-864.
71. Tsodyks M., Pawelzik K., Markram H. Neural networks with dynamic synapses. Neural Computation 1998, 10(4):821-835.
72. van Schaik, A., & Shih-Chii Liu, L. (2005). AER EAR: a matched silicon cochlea pair with address event representation interface. In Proc. of ISCAS-IEEE int. symp. on circuits and systems, Vol. 5 (pp. 4213-4216). May.
73. Verstraeten D., Schrauwen B., D'Haene M., Stroobandt D. An experimental unification of reservoir computing methods. Neural Networks 2007, 20(3):391-403.
74. Wang X., Hou Z.-G., Tan M., Wang Y., Lv F., Kasabov N. Mobile robots' target-reaching controller based on spiking neural networks. LNCS 2012, Springer, Qatar.
75. Watts M. A decade of Kasabov's evolving connectionist systems: a review. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews 2009, 39(3):253-269.
76. Wolpaw J., McFarland D., Vaughan T. Brain-computer interface research at the wadsworth center. IEEE Transactions on Rehabilitation Engineering 2000, 8(2):222-226.
77. Wysoski S., Benuskova L., Kasabov N. Evolving spiking neural networks for audiovisual information processing. Neural Networks 2010, 23(7):819-835.