Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

Frontiers in Neuroscience

Volumn , Issue 7 AUG, 2013, Pages

  Tapson, Jonathan C ^a   Cohen, Greg K ^a   Afshar, Saeed ^a   Stiefel, Klaus M ^a   Buskila, Yossi ^a   Wang, Runchun Mark ^a   Hamilton, Tara J ^a   van Schaik, André ^a  

^a WESTERN SYDNEY UNIVERSITY   (Australia)

Author keywords

Kernel method; Pseudoinverse solution; Spatio temporal spike pattern recognition; Spike time encoded information; Spiking network synthesis

Indexed keywords

ADULT; ARTICLE; BRAIN FUNCTION; BRAIN NERVE CELL; CONNECTOME; DENDRITIC CELL; HUMAN; INFORMATION PROCESSING; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; NERVE CELL MEMBRANE; NERVE CELL MEMBRANE POTENTIAL; NERVE CELL NETWORK; NERVE FIBER; NERVE FUNCTION; NERVOUS SYSTEM PARAMETERS; NEURAL NETWORK SYNTHESIS; PATTERN RECOGNITION; PHYSICAL PARAMETERS; SIGNAL PROCESSING; SPATIO TEMPORAL SPIKE PATTERN RECOGNITION; SPATIOTEMPORAL ANALYSIS; SYNAPTIC TRANSMISSION; SYNTHESIS;

EID: 84888815818     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2013.00153     Document Type: Article

References (46)

1. Basu, A., Shuo, S., Zhou, H., Hiot Lim, M., Huang, G.-B. (2013). Silicon spiking neurons for hardware implementation of extreme learning machines. Neuro computing 102, 125-132. doi: 10.1016/j.neucom.2012.01.042
2. Boahen, K. (2006). "Neurogrid: emulating a million neurons in the cortex," in Proceedings of the 28th IEEE Engineering in Medicine and Biology Society Conference (EMBS 2006), (New York, NY), 6702.
3. Chechik, G., Meilijson, I., and Ruppin, E. (1999). Neuronal Regulation: a mechanism for synaptic pruning during brain maturation. Neural Comput. 11, 2061-2080. doi: 10.1162/089976699300016089
4. Choudhary, S., Sloan, S., Fok, S., Neckar, A., Trautmann, E., Gao, P., et al. (2012). "Silicon neurons that compute," in International Conference Artificial Neural Networks, ICANN 2012, (Lausanne).
5. Conradt, J., Stewart, T., Eliasmith, C., Horiuchi, T., Tapson, J., Tripp, B., et al. (2012). "Spiking ratSLAM: Rat hippocampus cells in spiking neural hardware," in Proceedings of the IEEE Biomedical Circuits and Systems (BioCAS 2012), (Hsinchu), 91.
6. Drachman, D. A. (2004). Do we have brain to spare? Neurology 64, 2004-2005. doi: 10.1212/01.WNL. 0000166914.38327.BB
7. Eliasmith, C., and Anderson, C. H. (2003). Neural Engineering: Computation, Representation and Dynamics in Neurobiological Systems. Boston, MA: MIT Press.
8. Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., Dewolf, T., Tang, Y., et al. (2012). A large-scale model of the functioning brain. Science 338, 1202-1205. doi: 10.1126/science. 1225266
9. Galluppi, F., Davies, S., Furber, S., Stewart, T., and Eliasmith, C. (2012). "Real Time on-chip implementation of dynamical systems with spiking neurons," in Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2012, (Brisbane, QLD).
10. Gütig, R., and Sompolinksy, H. (2006). The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9, 420-428. doi: 10.1038/nn1643
11. Gütig, R., and Sompolinksy, H. (2009). Time-warp-invariant neuronal processing. PLoS Biol. 7:e1000141. doi: 10.1371/journal.pbio.1000141
12. Häusser, M., Spruston, N., and Stuart, G. J. (2000). Diversity and dynamics of dendritic signaling. Science 290, 739-744. doi: 10.1126/science.290.5492.739
13. Hopfield, J. J., and Brody, C. D. (2000). What is a moment? "Cortical" sensory integration over a brief interval. Proc. Natl. Acad Sci. U.S.A. 97, 13919-13924. doi: 10.1073/pnas.250483697
14. Hopfield, J. J., and Brody, C. D. (2001). What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc. Natl. Acad. Sci. 98, 1282-1287. doi: 10.1073/pnas.98.3.1282
15. Hopfield, J. J., and Brody, C. D. (2013). Mus silicium Competition - Results and Discussion. Available online at: http://web.archive.org/web/ 20031205135123/http://neuron.prin ceton.edu/~moment/Organism/Do cs/winners.html
16. Huang, G.-B., and Chen, L. (2008). Enhanced random search based incremental extreme learning machine. Neurocomputing 71, 3460-3468. doi: 10.1016/j.neucom. 2007.10.008
17. Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2006a). Extreme learning machine: theory and applications. Neurocomputing 70, 489-501. doi: 10.1016/j.neucom.2005.12.126
18. Huang, G.-B., Chen, L., and Siew, C.- K. (2006b). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17, 879-892. doi: 10.1109/TNN.2006.875977
19. Hussein, S., Basu, A., Wang, M., and Hamilton, T. J. (2012). "DELTRON: Neuromorphic architectures for delay based learning," in 2012 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 304-307. doi: 10.1109/APCCAS.2012.6419032
20. Indiveri, G. (2001). A current-mode hysteretic winner-take-all network, with excitatory and inhibitory coupling. Analog Integr. Circuits Signal Process. 28, 279-291. doi: 10.1023/A:1011208127849
21. Izhikevich, E. (2006). Polychronization: computation with spikes. Neural Comput. 18, 245-282. doi: 10.1162/089976606775093882
22. Jaeger, H. (2003). "Adaptive nonlinear system identification with echo state networks," in Advances in Neural Information Processing Systems 15, eds S. Becker, S. Thrun, and K. Obermayer (Cambridge, MA: MIT Press), 593-600.
23. Kay, S. M., and Marple, S. L. Jr. (1981). Spectrum Analysis: a modern perspective, Proc. IEEE Spectr. 69, 1380-1419.
24. Khan, M. M., Lester, D. R., Plana, L. A., Rast, A., Jin, A. X., Painkras, E., et al. (2008). "SpiNNaker: mapping neural networks onto a massivelyparallel chip multiprocessor," in Proceedings of the International Joint Conference on Neural Networks (ICJNN), (Hong Kong), 2849-2856.
25. Levy, W. B., and Baxter, R. A. (1996). Energy efficient neural codes. Neural Comput. 8, 531-543. doi: 10.1162/neco.1996.8.3.531
26. Maass, W., and Markram, H. (2004). On the computational power of recurrent circuits of spiking neurons. J. Comput. Syst. Sci. 69, 593-616. doi: 10.1016/j.jcss.2004. 04.001
27. Maass, W., Natschläger, T., and Markram, H. (2002). Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531-2560. doi: 10.1162/089976602760407955
28. Maass, W., and Sontag, E. D. (2000). Neural systems as nonlinear filters. Neural Comput. 12, 1743-1772. doi: 10.1162/089976600300015123
29. Masquelier, T., Guyonneau, R., and Thorpe, S. (2006). Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 1:e1377. doi: 10.1371/journal.pone.0001377
30. Masquelier, T., Guyonneau, R., and Thorpe, S. (2009). Competitive STDP-based spike pattern learning. Neural Comput. 21, 1259-1276. doi: 10.1162/neco.2008.06-08-804
31. Masuda, N., and Aihara, K. (2003). Duality of rate coding and temporal coding in multilayered feedforward networks. Neural Comput. 15, 103-125. doi: 10.1162/089976603321043711
32. Mohemmed, A., Schliebs, S., Matsuda, S., and Kasabov, N. (2012). Span: spike pattern association neuron for learning spatiotemporal spike patterns. Int. J. Neural Syst. 22, 1250012. doi: 10.1142/S0129065712500128
33. Paolicelli, R. C., Bolasco, G., Pagani, F., Maggi, L., Scianni, M., Panzanelli, P., et al. (2011). Pruning by microglia is necessary for normal brain development. Science 333, 1456-1458. doi: 10.1126/science.1202529
34. Ponulak, F., and Kasiñski, A. (2010). Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting. Neural Comput. 22, 467-510. doi: 10.1162/neco.2009.11-08-901
35. Rahimi, A., and Recht, B. (2009). "Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning," in Advances in Neural Information Processing Systems, Vol. 21. eds D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou (Cambridge, MA: MIT Press), 1313-1320.
36. Russell, A., Orchard, G., Dong, Y., Mihalas, S., Niebur, E., Tapson, J., et al. (2010). Optimization methods for spiking neurons and networks. IEEE Trans. Neural Netw. 21, 1950-1962. doi: 10.1109/TNN.2010.2083685
37. Saxe, A., Koh, P., Chen, Z., Bhand, M., Suresh, B., and Ng, A. (2011). "On random weights and unsupervised feature learning," in Proceedings of the 28th International Conference on Machine Learning, (Bellevue).
38. Schemmel, J., Bruederle, D., Gruebl, A., Hock, M., Meier, K., and Millner, S. (2010). "A Wafer-Scale neuromorphic hardware system for large-scale neural modeling," in Proceedings of the 2010 IEEE International Symposium on Circuits and Systems (ISCAS), (Paris), 1947-1950. doi: 10.1109/ISCAS.2010.5536970
39. Tapson, J., and van Schaik, A. (2013). Learning the pseudoinverse solution to network weights. Neural Netw. 45, 94-100. doi: 10.1016/j.neunet. 2013.02.008
40. Tapson, J., Jin, C., van Schaik, A., and Etienne-Cummings, R. (2009). A first-order nonhomogeneous Markov model for the response of spiking neurons stimulated by small phase-continuous signals. Neural Comput. 21, 1554-1588. doi: 10.1162/neco. 2009.06-07-548
41. TI46, 2013. The TI46 spoken digits corpus is available from the Linguistic Data Consortium. Available online at: http://ccl.pku.edu.cn/doubtfire/ CorpusLinguistics/LDC_Corpus/ava ilable_corpus_from_ldc.html#ti46
42. Torben-Nielsen, B., and Stiefel, K. M. (2010). An inverse approach for elucidating dendritic function. Front. Comput. Neurosci. 4:128. doi: 10.3389/fncom.2010.00128
43. Van Rullen, R., and Thorpe, S. J. (2001). Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput. 13, 1255-1283. doi: 10.1162/08997660152002852
44. Wills, S. (2004). Computation with Spiking Neurons. PhD. thesis, University of Cambridge. Available online at http://www.inference.phy. cam.ac.uk/saw27/thesis.pdf
45. Wills, S. (2001). Recognising Speech with Biologically-Plausible Processors, Cavendish Laboratory Report. Cambridge: Cambridge University.
46. Zamarreño-Ramos, C., Linares- Barranco, A., Serrano-Gotarredona, T., and Linares-Barranco, B. (2013). Multi-Casting Mesh AER: a scalable assembly approach for reconfigurable neuromorphic structured AER Systems. Application to ConvNets. IEEE Trans. Biomed. Circuits Syst. 7, 82-102. doi: 10.1109/TBCAS.2012.2195725