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output codes. Adv. Neural Inf. Process. Syst. pp. 1410–1418 (2009)
9. C.D. Schuman, T.E. Potok, R.M. Patton, J.D. Birdwell, M.E. Dean, G.S. Rose, J.S. Plank,
A survey of neuromorphic computing and neural networks in hardware. arXiv preprint
arXiv:1705.06963 (2017)
10. G.W. Burr, R.M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M.i Ishii,
P. Narayanan, A. Fumarola, et al., Neuromorphic computing using non-volatile memory. Adv.
Phys.: X 2(1), 89–124 (2017)
11. G. Indiveri, S.-C. Liu, Memory and information processing in neuromorphic systems. Proc.
IEEE 103(8), 1379–1397 (2015)
12. M. Ziegler, Ch. Wenger, E. Chicca, H. Kohlstedt, Tutorial: Concepts for closely mimicking
biological learning with memristive devices: Principles to emulate cellular forms of learning.
J. Appl. Phys. 124(15), 152003 (2018)
13. T. Delbrück B. Linares-Barranco, E. Culurciello, C. Posch, Activity-driven, event-based vision
sensors, in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, May
2010, pp. 2426–2429
14. C. Brandli, R. Berner, M. Yang, S.C. Liu, T. Delbruck, A 240× 180 130 dB 3 μs latency global
shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49(10), 2333–2341 (2014).
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15. S.C. Liu, A. van Schaik, B.A. Minch, T. Delbruck, Asynchronous binaural spatial audition
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Aug
16. A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T Nayak, A. Andreopoulos,
G. Garreau, M. Mendoza, J. Kusnitz, M. Debole, S. Esser, T. Delbruck, M. Flickner, D. Modha,
A low power, fully event-based gesture recognition system, in IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), July 2017, pp. 7388–7397
17. T. Serrano-Gotarredona, B. Linares-Barranco, F. Galluppi, L. Plana, S. Furber, Convnets experiments on spinnaker, in 2015 IEEE International Symposium on Circuits and Systems (ISCAS),
May 2015, pp. 2405–2408
18. T. Kohonen, The self-organizing map. Neurocomputing 21(1–3), 1–6 (1998)
19. J. Wu, Y. Chua, M. Zhang, H. Li, K.C. Tan, A spiking neural network framework for robust
sound classification. Front. Neurosci. 12, 836 (2018)
20. M. Bear, B. Connors, M. Paradiso, Neuroscience: Exploring the brain, 4th edn. (Wolters
Kluwer, Philadelphia, 2016)
21. A. R. Møller, Hearing: Anatomy, Physiology, and Disorders of the Auditory System, (Plural
Publishing, 2012)
22. Q. Yu, H. Tang, K.C. Tan, H. Li, Rapid feedforward computation by temporal encoding and
learning with spiking neurons. IEEE Trans. Neural Networks Learn. Syst. 24(10), 1539–1552
(2013)
23. S.M. Bohte, J.N. Kok, H. La Poutre, Error-backpropagation in temporally encoded networks
of spiking neurons. Neurocomputing 48(1–4), 17–37 (2002)
24. C. Pantev, O. Bertrand, C. Eulitz, C. Verkindt, S. Hampson, G. Schuierer, T. Elbert, Specific
tonotopic organizations of different areas of the human auditory cortex revealed by simultaneous magnetic and electric recordings. Electroencephalogr. Clin. Neurophysiol. 94(1), 26–40
(1995)
25. R. Gütig, H. Sompolinsky, The tempotron: a neuron that learns spike timing-based decisions.
Nat. Neurosci. 9(3), 420 (2006)
26. J. Dennis, Q. Yu, H. Tang, H.D. Tran, H. Li, Temporal coding of local spectrogram features for
robust sound recognition, in 2013 IEEE International Conference on Acoustics, Speech and
Signal Processing, May 2013, pp. 803–807
27. R. Xiao, R. Yan, H. Tang, K.C. Tan, A Spiking Neural Network Model for Sound Recognition
(Springer, Singapore, 2017), pp. 584–594
28. J. Wu, Y. Chua, H. Li, A biologically plausible speech recognition framework based on spiking
neural networks, in 2018 International Joint Conference on Neural Networks (IJCNN), July
2018, pp. 1–8
J. Wu et al.
8. M. Palatucci, D. Pomerleau, G.E. Hinton, T.M. Mitchell, Zero-shot learning with semantic
output codes. Adv. Neural Inf. Process. Syst. pp. 1410–1418 (2009)
9. C.D. Schuman, T.E. Potok, R.M. Patton, J.D. Birdwell, M.E. Dean, G.S. Rose, J.S. Plank,
A survey of neuromorphic computing and neural networks in hardware. arXiv preprint
arXiv:1705.06963 (2017)
10. G.W. Burr, R.M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M.i Ishii,
P. Narayanan, A. Fumarola, et al., Neuromorphic computing using non-volatile memory. Adv.
Phys.: X 2(1), 89–124 (2017)
11. G. Indiveri, S.-C. Liu, Memory and information processing in neuromorphic systems. Proc.
IEEE 103(8), 1379–1397 (2015)
12. M. Ziegler, Ch. Wenger, E. Chicca, H. Kohlstedt, Tutorial: Concepts for closely mimicking
biological learning with memristive devices: Principles to emulate cellular forms of learning.
J. Appl. Phys. 124(15), 152003 (2018)
13. T. Delbrück B. Linares-Barranco, E. Culurciello, C. Posch, Activity-driven, event-based vision
sensors, in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, May
2010, pp. 2426–2429
14. C. Brandli, R. Berner, M. Yang, S.C. Liu, T. Delbruck, A 240× 180 130 dB 3 μs latency global
shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49(10), 2333–2341 (2014).
Oct
15. S.C. Liu, A. van Schaik, B.A. Minch, T. Delbruck, Asynchronous binaural spatial audition
sensor with 2644 channel output. IEEE Trans. Biomed. Circuits Syst. 8(4), 453–464 (2014).
Aug
16. A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T Nayak, A. Andreopoulos,
G. Garreau, M. Mendoza, J. Kusnitz, M. Debole, S. Esser, T. Delbruck, M. Flickner, D. Modha,
A low power, fully event-based gesture recognition system, in IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), July 2017, pp. 7388–7397
17. T. Serrano-Gotarredona, B. Linares-Barranco, F. Galluppi, L. Plana, S. Furber, Convnets experiments on spinnaker, in 2015 IEEE International Symposium on Circuits and Systems (ISCAS),
May 2015, pp. 2405–2408
18. T. Kohonen, The self-organizing map. Neurocomputing 21(1–3), 1–6 (1998)
19. J. Wu, Y. Chua, M. Zhang, H. Li, K.C. Tan, A spiking neural network framework for robust
sound classification. Front. Neurosci. 12, 836 (2018)
20. M. Bear, B. Connors, M. Paradiso, Neuroscience: Exploring the brain, 4th edn. (Wolters
Kluwer, Philadelphia, 2016)
21. A. R. Møller, Hearing: Anatomy, Physiology, and Disorders of the Auditory System, (Plural
Publishing, 2012)
22. Q. Yu, H. Tang, K.C. Tan, H. Li, Rapid feedforward computation by temporal encoding and
learning with spiking neurons. IEEE Trans. Neural Networks Learn. Syst. 24(10), 1539–1552
(2013)
23. S.M. Bohte, J.N. Kok, H. La Poutre, Error-backpropagation in temporally encoded networks
of spiking neurons. Neurocomputing 48(1–4), 17–37 (2002)
24. C. Pantev, O. Bertrand, C. Eulitz, C. Verkindt, S. Hampson, G. Schuierer, T. Elbert, Specific
tonotopic organizations of different areas of the human auditory cortex revealed by simultaneous magnetic and electric recordings. Electroencephalogr. Clin. Neurophysiol. 94(1), 26–40
(1995)
25. R. Gütig, H. Sompolinsky, The tempotron: a neuron that learns spike timing-based decisions.
Nat. Neurosci. 9(3), 420 (2006)
26. J. Dennis, Q. Yu, H. Tang, H.D. Tran, H. Li, Temporal coding of local spectrogram features for
robust sound recognition, in 2013 IEEE International Conference on Acoustics, Speech and
Signal Processing, May 2013, pp. 803–807
27. R. Xiao, R. Yan, H. Tang, K.C. Tan, A Spiking Neural Network Model for Sound Recognition
(Springer, Singapore, 2017), pp. 584–594
28. J. Wu, Y. Chua, H. Li, A biologically plausible speech recognition framework based on spiking
neural networks, in 2018 International Joint Conference on Neural Networks (IJCNN), July
2018, pp. 1–8