Real-time classification and sensor fusion with a spiking deep belief network

Frontiers in Neuroscience

Volumn , Issue 7 OCT, 2013, Pages

  O'Connor, Peter ^a   Neil, Daniel ^a   Liu, Shih Chii ^a   Delbruck, Tobi ^a   Pfeiffer, Michael ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

Deep belief networks; Deep learning; Generative model; Sensory fusion; Silicon cochlea; Silicon retina; Spiking neural network

Indexed keywords

SILICON;

ARTICLE; ASSOCIATION; COCHLEA; COMPUTER PROGRAM; COMPUTER SIMULATION; DEEP BELIEF NETWORK; DIGITAL COMPUTER; MACHINE LEARNING; NERVE CELL; NOISE; RECOGNITION; REFRACTORY PERIOD; RETINA; ROTATION; SENSORY STIMULATION; SPIKING NEURAL NETWORK; TASK PERFORMANCE;

EID: 84888811418     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2013.00178     Document Type: Article

References (59)

1. Acero, A., Bernstein, N., Chambers, R., Ju, Y.-C., Li, X., Odell, J., et al. (2008). "Live search for mobile: web services by voice on the cellphone," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE), (Las Vegas, NV), 5256-5259.
2. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006). "Greedy layer-wise training of deep networks," in Advances in Neural Information Processing Systems 19. Vancouver: MIT Press.
3. Bullier, J. (2001). Integrated model of visual processing. Brain Res. Rev. 36, 96-107. doi: 10.1016/S0165-0173(01)00085-6
4. Büsing, L., Bill, J., Nessler, B., and Maass, W. (2011). Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput. Biol. 7: e1002211. doi: 10.1371/journal.pcbi.1002211
5. Camuñas Mesa, L., Pérez-Carrasco, J., Zamarreño Ramos, C., Serrano-Gotarredona, T., and Linares-Barranco, B. (2010). "On scalable spiking ConvNet hardware for cortex-like visual sensory processing systems," in Processing of IEEE International Symposium on Circuits and Systems (ISCAS), (Paris), 249-252.
6. Ciresan, D. C., Meier, U., Gambardella, L. M., and Schmidhuber, J. (2010). Deep, big, simple neural nets for handwritten digit recognition. Neural Comput. 22, 3207-3220. doi: 10.1162/NECO_a_00052
7. da Costa, N. M., and Martin, K. A. C. (2009). The proportion of synapses formed by the axons of the lateral geniculate nucleus in layer 4 of area 17 of the cat. J. Comp. Neurol. 516, 264-276. doi: 10.1002/cne.22133
8. Dahl, G. E., Yu, D., Deng, L., and Acero, A. (2012). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20, 30-42. doi: 10.1109/TASL.2011.2134090
9. Delbruck, T. (2013). jAER Open Source Project. Available online at: http://sourceforge.net/apps/trac/jaer/wiki.
10. Desimone, R., Albright, T. D., Gross, C. G., and Bruce, C. (1984). Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci. 4, 2051-2062. doi: 10.1007/s12021-011-9106-1
11. Douglas, R. J., and Martin, K. A. C. (2011). What's black and white about the grey matter? Neuroinformatics 9, 167-179. doi: 10.1007/s12021-011-9106-1
12. Erhan, D., Bengio, Y., Courville, A., Manzagol, P., Vincent, P., and Bengio, S. (2010). Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625-660.
13. Falchier, A., Clavagnier, S., Barone, P., and Kennedy, H. (2002). Anatomical evidence of multimodal integration in primate striate cortex. J. Neurosci. 22, 5749-5759.
14. Farabet, C., Paz, R., Pérez-Carrasco, J., Zamarreño, C., Linares-Barranco, A., LeCun, Y., et al. (2012). Comparison between frame-constrained fix-pixel-value and frame-free spiking-dynamic-pixel ConvNets for visual processing. Front. Neurosci. 6: 32. doi: 10.3389/fnins.2012.00032
15. Felleman, D. J., and Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1-47. doi: 10.1093/cercor/1.1.1
16. Friston, K. (2010). The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127-138. doi: 10.1038/nrn2787
17. Gerstner, W., and Kistler, W. (2002). Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511815706
18. Goh, H., Thome, N., and Cord, M. (2010). "Biasing restricted Boltzmann machines to manipulate latent selectivity and sparsity," in NIPS workshop on deep learning and unsupervised feature learning, (Whistler, BC).
19. Gross, C. G., Roche-Miranda, G. E., and Bender, D. B. (1972). Visual properties of neurons in the inferotemporal cortex of the macaque. J. Neurophysiol. 35, 96-111.
20. Hadsell, R., Sermanet, P., Ben, J., Erkan, A., Scoffier, M., Kavukcuoglu, K., et al. (2009). Learning long-range vision for autonomous off-road driving. J. Field Robot. 26, 120-144. doi: 10.1002/rob.20276
21. Hawkins, J., and Blakeslee, S. (2004). On intelligence. New York, NY: Times Books.
22. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82-97. doi: 10.1109/MSP.2012.2205597
23. Hinton, G., Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527-1554. doi: 10.1162/neco.2006.18.7.1527
24. Hinton, G. E., and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science 313, 504-507. doi: 10.1126/science.1127647
25. Hinton, G. E., and Sejnowski, T. J. (1986). Learning and Relearning in Boltzmann Machines. Cambridge, MA: MIT Press 1, 282-317.
26. Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., and Elvezia, C. (2001). "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies," in A Field Guide to Dynamical Recurrent Neural Networks. eds S. C. Kremer and J. F. Kolen (New York, NY: IEEE Press), 237-244.
27. Hochstein, S., and Ahissar, M. (2002). View from the top: hierarchies and reverse hierarchies review. Neuron 36, 791-804. doi: 10.1016/S0896-6273(02)01091-7
28. Indiveri, G., Linares-Barranco, B., Hamilton, T., van Schaik, A., Etienne-Cummings, R., Delbruck, T., et al. (2011). Neuromorphic silicon neuron circuits. Front. Neurosci. 5: 73. doi: 10.3389/fnins.2011.00073
29. Jug, F., Cook, M., and Steger, A. (2012). "Recurrent competitive networks can learn locally excitatory topologies," in International Joint Conference on Neural Networks (IJCNN), (Brisbane), 1-8.
30. Kayser, C., and Logothetis, N. K. (2007). Do early sensory cortices integrate cross-modal information? Brain Struct. Funct. 212, 121-132. doi: 10.1007/s00429-007-0154-0
31. Kersten, D., and Yuille, A. (2003). Bayesian models of object perception. Curr. Opin. Neurobiol. 13, 150-158. doi: 10.1016/S0959-4388(03)00042-4
32. Knill, D. C., and Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712-719. doi: 10.1016/j.tins.2004.10.007
33. Kosslyn, S. M., Pascual-Leone, A., Felician, O., Camposano, S., Keenan, J. P., Thompson, W. L., et al. (1999). The role of Area 17 in visual imagery: Convergent evidence from PET and rTMS. Science 284, 167-170. doi: 10.1126/science.284.5411.167
34. Lamme, V. A. F., Supér, H., and Spekreijse, H. (1998). Feedforward, horizontal, and feedback processing in the visual cortex. Curr. Opin. Neurobiol. 8, 529-535. doi: 10.1016/S0959-4388(98)80042-1
35. Larochelle, H., Erhan, D., Courville, A., Bergstra, J., and Bengio, Y. (2007). "An empirical evaluation of deep architectures on problems with many factors of variation," in Proceedings of ICML, 473-480. ACM.
36. Le, Q. V., Ranzato, M. A., Monga, R., Devin, M., Chen, K., Corrado, G., et al. (2012). "Building high-level features using large scale unsupervised learning," in Proceedings of ICML, (Edinburgh).
37. LeCun, Y. L., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278-2324. doi: 10.1109/5.726791
38. Lee, H., Ekanadham, C., and Ng, A. (2008). "Sparse deep belief net model for visual area V2," in Advances in Neural Information Processing Systems, Vol 20, (Vancouver), 873-880.
39. Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. (2009). "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in Proceedings of ICML, (Montreal), 609-616. doi: 10.1145/1553374.1553453
40. Lichtsteiner, P., Posch, C., and Delbruck, T. (2008). A 128× 128 120 db 15 μs latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circ. 43, 566-576. doi: 10.1109/JSSC.2007.914337
41. Liu, S., Van Schaik, A., Minch, B., and Delbruck, T. (2010). "Event-based 64-channel binaural silicon cochlea with Q enhancement mechanisms," in Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), (Paris), 2027-2030.
42. Markov, N., and Kennedy, H. (2013). The importance of being hierarchical. Curr. Opin. Neurobiol. 23, 187-194. doi: 10.1016/j.conb.2012.12.008
43. Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., et al. (2012). A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 1-20. doi: 10.1093/cercor/bhs270
44. Merolla, P., Ursell, T., and Arthur, J. (2010). The Thermodynamic Temperature of a Rhythmic Spiking Network. Arxiv preprint arXiv: 1009.5473.
45. MIT Technology Review (2013). 10 breakthrough technologies 2013: Deep learning. Available online at: http://www.technologyreview.com/featuredstory/513696/deep-learning/
46. Mohamed, A.-R., Dahl, G. E., and Hinton, G. (2012). Acoustic modeling using deep belief networks. IEEE Trans. Audio Speech Lang. Process. 20, 14-22. doi: 10.1109/TASL.2011.2109382
47. Murray, S. O., Kersten, D., Olshausen, B. A., Schrater, P., and Woods, D. L. (2002). Shape perception reduces activity in human primary visual cortex. Proc. Natl. Acad. Sci. U.S.A. 99, 15164-15169. doi: 10.1073/pnas.192579399
48. Nair, V., and Hinton, G. (2010). "Rectified linear units improve Restricted Boltzmann Machines," in Proceedings of ICML, (Haifa), 807-814.
49. Nessler, B., Pfeiffer, M., Buesing, L., and Maass, W. (2013). Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Comput. Biol. 9: e1003037. doi: 10.1371/journal.pcbi.1003037
50. Pérez-Carrasco, J., Zhao, B., Serrano, C., Acha, B., Serrano-Gotarredona, T., Chen, S., et al. (2013). Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate-coding and coincidence processing. application to feed forward ConvNets. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2706-2719. doi: 10.1109/TPAMI.2013.71
51. Rao, R. P. N., and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79-87. doi: 10.1038/4580
52. Riesenhuber, M., and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019-1025. doi: 10.1038/14819
53. Seide, F., Li, G., and Yu, D. (2011). "Conversational speech transcription using context-dependent deep neural networks," in Proceedings of Interspeech, (Florence), 437-440.
54. Siegert, A. J. F. (1951). On the first passage time probability problem. Phys. Rev. 81: 617. doi: 10.1103/PhysRev.81.617
55. Stein, B. E., and Stanford, T. R. (2008). Multisensory integration: current issues from the perspective of the single neuron. Nat. Rev. Neurosci. 9, 255-266. doi: 10.1038/nrn2331
56. Taylor, G., Hinton, G., and Roweis, S. (2007). "Modeling human motion using binary latent variables," in Advances in Neural Information Processing Systems, (Vancouver), 1345-1352.
57. Tieleman, T. (2008). "Training restricted Boltzmann machines using approximations to the likelihood gradient," in Proceedings of ICML, (Helsinki: ACM), 1064-1071.
58. Tieleman, T., and Hinton, G. (2009). "Using fast weights to improve persistent contrastive divergence," in Proceedings of ICML, (Montreal: ACM), 1033-1040.
59. Van Essen, D. C., and Maunsell, J. H. R. (1983). Hierarchical organization and functional streams in the visual cortex. Trends Neurosci. 6, 370-375. doi: 10.1016/0166-2236(83)90167-4