A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection

Neural Networks

Volumn 22, Issue 10, 2009, Pages 1419-1431

  Ghosh Dastidar, Samanwoy ^a   Adeli, Hojjat ^a,b  

^a Ohio State University   (United States)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

EEG classification; Epilepsy; Spiking neural networks; Supervised learning

Indexed keywords

CLASSIFICATION ACCURACY; EEG CLASSIFICATION; EPILEPSY; HEURISTIC RULES; MODULAR ARCHITECTURES; OPTIMUM PARAMETER VALUES; ORDERS OF MAGNITUDE; SEIZURE DETECTION; SPIKING NEURAL NETWORKS;

COMPUTER AIDED DIAGNOSIS; EDUCATION; ELECTROENCEPHALOGRAPHY; HEURISTIC ALGORITHMS; INTELLIGENT AGENTS; LEARNING ALGORITHMS; SUPERVISED LEARNING;

NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DIAGNOSTIC VALUE; ELECTROENCEPHALOGRAM; EPILEPSY; HUMAN; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; NERVE CELL; POSTSYNAPTIC POTENTIAL; PRIORITY JOURNAL; SEIZURE; SPIKE; SYNAPSE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); RECEPTORS, PRESYNAPTIC; SEIZURES;

EID: 71049128082     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.04.003     Document Type: Article

References (71)

1. Abbott L.F., and Kepler T.B. Model neurons: From Hodgkin-Huxley to Hopfield. In: Garrido L. (Ed). Statistical mechanics of neural networks (1990), Springer, Berlin
2. Adeli H., and Hung S.L. An adaptive conjugate gradient learning algorithm for effective training of multilayer neural networks. Applied Mathematics and Computation 62 1 (1994) 81-102
3. Adeli H., and Hung S.L. Machine learning - Neural networks, genetic algorithms, and fuzzy sets (1995), John Wiley and Sons, NY
4. Adeli H., and Jiang X. Neuro-fuzzy logic model for freeway work zone capacity estimation. Journal of Transportation Engineering 129 5 (2003) 484-493
5. Adeli H., and Jiang X. Dynamic fuzzy wavelet neural network model for structural system identification. Journal of Structural Engineering 132 1 (2006) 102-111
6. Adeli H., and Karim A. Fuzzy-wavelet RBFNN model for freeway incident detection. Journal of Transportation Engineering 126 6 (2000) 464-471
7. Adeli H., Ghosh-Dastidar S., and Dadmehr N. A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering 54 2 (2007) 205-211
8. Adeli H., and Park H.S. Counter propagation neural network in structural engineering. Journal of Structural Engineering 121 8 (1995) 1205-1212
9. Andrzejak R.G., Lehnertz K., Rieke C., Mormann F., David P., and Elger C.E. Indications of non-linear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E 64 6 (2001) 0619071-8
10. Bohte S.M., Kok J.N., and La Poutré J.A. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48 1-4 (2002) 17-37
11. Booij O., and Nguyen H.T. A gradient descent rule for multiple spiking neurons emitting multiple spikes. Information Processing Letters 95 6 (2005) 552-558
12. Bose N.K., and Liang P. Neural network fundamentals with graphs, algorithms and applications (1996), McGraw Hill, NY
13. Carpenter G.A., and Grossberg S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing 37 (1987) 54-115
14. Dharia A., and Adeli H. Neural network model for rapid forecasting of freeway link travel time. Engineering Applications of Artificial Intelligence 16 7-8 (2003) 607-613
15. Ermentrout G.B. Type I membranes, phase resetting curves, and synchrony. Neural Computation 8 (1996) 979-1001
16. Fisher R.A. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7 (1936) 179-188
17. Ermentrout G.B., and Kopell N. Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM Journal on Applied Mathematics 46 (1986) 233-253
18. Gerstner W. Time structure of the activity in neural network models. Physical Review E 51 (1995) 738-758
19. Gerstner W., and Kistler W.M. Spiking neuron models. Single neurons, populations, plasticity (2002), Cambridge University Press, NY
20. Ghosh-Dastidar S., and Adeli H. Neural network-wavelet microsimulation model for delay and queue length estimation at freeway work zones. Journal of Transportation Engineering 132 4 (2006) 331-341
21. Ghosh-Dastidar S., and Adeli H. Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integrated Computer-Aided Engineering 14 3 (2007) 187-212
22. Ghosh-Dastidar S., Adeli H., and Dadmehr N. Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering 54 9 (2007) 1545-1551
23. Ghosh-Dastidar S., Adeli H., and Dadmehr N. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Transactions on Biomedical Engineering 55 2 (2008) 512-518
24. Grossberg S. Studies of mind and brain (1982), Reidel Press, Boston
25. Grossberg S. Competitive learning: From interactive activation to adaptive resonance. In: Waltz D., and Feldman J.A. (Eds). Connectionist models and their implications: Readings from cognitive science (1988), Ablex, Norwood, NJ 243-283
26. Grossberg S., and Versace M. Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research 1218C (2008) 278-312
27. Hecht-Nielsen R. Application of counterpropagation networks. Neural Networks 1 2 (1988) 131-139
28. Hille B. Ionic channels of excitable membranes. 2nd ed. (1992), Sinauer Associates, Sunderland, MA
29. Hodgkin A.L., and Huxley A.F. A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. Journal of Physiology 117 (1952) 500-544
30. Hooshdar S., and Adeli H. Toward intelligent variable message signs in freeway work zones: A neural network approach. Journal of Transportation Engineering 130 1 (2004) 83-93
31. Hopfield J.J. Neural networks and physical systems with emergent collective computational properties. Proceedings of the National Academy of Sciences 79 (1982) 2554-2558
32. Hoppensteadt F.C., and Izhikevich E.M. Weakly connected neural networks (1997), Springer-Verlag, New York
33. Hung S.L., and Adeli H. Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer. Neurocomputing 5 6 (1993) 287-302
34. Hung S.L., and Adeli H. Object-oriented back propagation and its application to structural design. Neurocomputing 6 1 (1994) 45-55
35. Iglesias J., and Villa A.E.P. Emergence of preferred firing sequences in large spiking neural networks during simulated neuronal development. International Journal of Neural Systems 18 4 (2008) 267-277
36. Izhikevich E.M. Resonate-and-fire neurons. Neural Networks 14 (2001) 883-894
37. Izhikevich E.M. Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral Cortex 17 (2007) 2443-2452
38. Jiang X., and Adeli H. Dynamic fuzzy wavelet neuroemulator for nonlinear control of irregular highrise building structures. International Journal for Numerical Methods in Engineering 74 7 (2008) 1045-1066
39. Kandel E.R., Schwartz J.H., and Jessell T.M. Principles of neural science. 4th ed. (2000), McGraw- Hill, NY
40. Karim A., and Adeli H. Comparison of the fuzzy-wavelet RBFNN freeway incident detection model with the California algorithm. Journal of Transportation Engineering 128 1 (2002) 21-30
41. Karim A., and Adeli H. Radial basis function neural network for work zone capacity and queue estimation. Journal of Transportation Engineering 129 5 (2003) 494-503
42. Kasinski A., and Ponulak F. Comparison of supervised learning methods for spike time coding in spiking neural networks. International Journal of Applied Mathematics and Computer Science 16 1 (2006) 101-113
43. Kepler T.B., Abbott L.F., and Marder E. Reduction of conductance-based neuron models. Biological Cybernetics 66 (1992) 381-387
44. Kistler W.M., Gerstner W., and van Hemmen J.L. Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural Computation 9 (1997) 1015-1045
45. Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics 43 (1982) 59-69
46. Kopell N., and Ermentrout G.B. Subcellular oscillations and bursting. Mathematical Biosciences 78 (1986) 265-291
47. Liu H., Wang X., and Qiang W. A fast method for implicit surface reconstruction based on radial basis functions network from 3D scattered points. International Journal of Neural Systems 17 6 (2007) 459-465
48. Maass W. Lower bounds for the computational power of spiking neural networks. Neural Computation 8 1 (1996) 1-40
49. Maass W. Networks of spiking neurons: The third generation of spiking neural network models. Neural Networks 10 9 (1997) 1659-1671
50. Maass W. Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons. In: Mozer M., Jordan M.I., and Petsche T. (Eds). Advances in neural information processing systems Vol. 9 (1997), MIT Press, Cambridge, MA 211-217
51. Maass W. Fast sigmoidal networks via spiking neurons. Neural Computation 9 2 (1997) 279-304
52. Mayorga R.V., and Carrera J. A radial basis function network approach for the computational of inverse continuous time variant functions. International Journal of Neural Systems 17 3 (2007) 149-160
53. Mazzoni P., Andersen R.A., and Jordan M.I. More biologically plausible learning rule than backpropagation applied to a network model of cortical area 7a. Cerebral Cortex 1 (1991) 293-307
54. McKennoch, S., Liu, D., & Bushnell, L. G. (2006). Fast modifications of the SpikeProp algorithm. In Proceedings of the international joint conference on neural networks (pp. 3970-3977)
55. Moore, S. C. (2002). Backpropagation in spiking neural networks. M.S. thesis. University of Bath
56. Natschläger T., and Ruf B. Spatial and temporal pattern analysis via spiking neurons. Network: Computation in Neural Systems 9 3 (1998) 319-332
57. Newman, D. J., Hettich, S., Blake, C. L., & Merz, C. J. (1998). UCI Repository of machine learning databases. Irvine, CA: Department of Information and Computer Science, University of California. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
58. Panakkat A., and Adeli H. Neural network models for earthquake magnitude prediction using multiple seismicity indicators. International Journal of Neural Systems 17 1 (2007) 13-33
59. Pedrycz W., Rai R., and Zurada J. Experience-consistent modeling for radial basis function neural networks. International Journal of Neural Systems 18 4 (2008) 279-292
60. Rigatos G.G. Adaptive fuzzy control with output feedback for H-infinity tracking of SISI nonlinear systems. International Journal of Neural Systems 18 4 (2008) 305-320
61. Rinzel J., and Ermentrout G.B. Analysis of neuronal excitability and oscillations. In: Koch C., and Segev I. (Eds). Methods in neuronal modeling (1989), MIT Press, Cambridge, MA 135-169
62. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel distributed processing Vol. 1 (1986), MIT Press, Cambridge, MA 318-362
63. Sabourin C., Madani K., and Bruneau O. Autonomous biped Gait pattern based on fuzzy-CMAC neural networks. Integrated Computer-Aided Engineering 14 2 (2007) 173-186
64. Schaefer A.M., and Zimmermann H.G. Recurrent neural networks are universal approximators. International Journal of Neural Systems 17 4 (2007) 253-263
65. Schrauwen, B., & van Campenhout, J. (2004). Extending SpikeProp. In Proceedings of the international joint conference on neural networks (pp. 471-476)
66. Sejnowski T.J. Open questions about computation in the cerebral cortex. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel distributed processing Vol. 2 (1986), MIT Press, Cambridge, MA 372-389
67. Silva, S. M., & Ruano, A. E. (2005). Application of Levenberg-Marquardt method to the training of spiking neural networks. In Proceedings of the international conference on neural networks and brain: Vol. 3 (pp. 1354-1358)
68. Sirca G., and Adeli H. Neural network model for uplift load capacity of metal roof panels. Journal of Structural Engineering 127 11 (2001) 1276-1285
69. Stork D.G. Is backpropagation biologically plausible?. Proceedings of the international joint conference on neural networks (1989), IEEE Press, New York II 241-246
70. Xin, J., & Embrechts, M. J. (2001). Supervised learning with spiking neural networks. In Proceedings of the international joint conference on neural networks: Vol. 3 (pp. 1772-1777)
71. Zhang B., Xu S., and Li Y. Delay-dependent robust exponential stability for uncertain recurrent neural networks with time-varying delays. International Journal of Neural Systems 17 3 (2007) 207-218