A VLSI neuromorphic device for implementing spike-based neural networks

Frontiers in Artificial Intelligence and Applications

Volumn 234, Issue , 2011, Pages 305-316

  Indiveri, Giacomo ^a   Chicca, Elisabetta ^a,b  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b BIELEFELD UNIVERSITY   (Germany)

Author keywords

Address Event Representation (AER); Integrate and Fire (I F) neuron; learning; Neuromorphic circuits; spike based plasticity; STDP; synapse; Winner Take All (WTA)

Indexed keywords

NEURAL NETWORKS; TIMING CIRCUITS; VLSI CIRCUITS;

ADDRESS-EVENT REPRESENTATION; INTEGRATE-AND-FIRE NEURONS; LEARNING; NEUROMORPHIC CIRCUITS; SPIKE-BASED PLASTICITY; STDP; SYNAPSE; WINNER-TAKE-ALL;

NEURONS;

EID: 85140208993     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: 10.3233/978-1-60750-972-1-305     Document Type: Conference Paper

References (40)

1. S. Amari and M.A. Arbib. Competition and cooperation in neural nets. In J. Metzler, editor, Systems Neuroscience, pages 119–165. Academic Press, 1977.
2. J. Arthur and K. Boahen. Learning in silicon: Timing is everything. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18. MIT Press, Cambridge, MA, 2006.
3. D. Badoni, M. Giulioni, V. Dante, and P. Del Giudice. An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses. In International Symposium on Circuits and Systems, ISCAS 2006, pages 1227–1230. IEEE, May 2006.
4. C. Bartolozzi and G. Indiveri. Synaptic dynamics in analog VLSI. Neural Computation, 19(10):2581–2603, Oct 2007.
5. R. Ben-Yishai, R. Lev Bar-Or, and H. Sompolinsky. Theory of orientation tuning in visual cortex. Proceedings of the National Academy of Sciences of the USA, 92(9):3844–3848, April 1995.
6. T. Binzegger, R.J. Douglas, and K. Martin. A quantitative map of the circuit of cat primary visual cortex. Journal of Neuroscience, 24(39):8441–53, 2004.
7. K.A. Boahen. Point-to-point connectivity between neuromorphic chips using address-events. IEEE Transactions on Circuits and Systems II, 47(5):416–34, 2000.
8. J. Brader, W. Senn, and S. Fusi. Learning real world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation, 19:2881–2912, 2007.
9. R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J.M. Bower, M. Diesmann, A. Morrison, P.H. Harris Jr. F.C. Goodman, M. Zirpe, T. Natschläger, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner, O. Rochel, T. Vieville, E. Muller, A.P. Davison, S. El Boustani, and A. Destexhe. Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3):349–398, December 2007.
10. E. Chicca, D. Badoni, V. Dante, M. D’Andreagiovanni, G. Salina, L. Carota, S. Fusi, and P. Del Giudice. A VLSI recurrent network of integrate–and–fire neurons connected by plastic synapses with long term memory. IEEE Transactions on Neural Networks, 14(5):1297–1307, September 2003.
11. E. Chicca, A.M. Whatley, P. Lichtsteiner, V. Dante, T. Delbruck, P. Del Giudice, R.J. Douglas, and G. Indiveri. A multi-chip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Transactions on Circuits and Systems I, 5(54):981–993, 2007.
12. T.Y. W. Choi, P.A. Merolla, J.V. Arthur, K.A. Boahen, and B.E. Shi. Neuromorphic implementation of orientation hypercolumns. IEEE Transactions on Circuits and Systems I, 52(6):1049–1060, 2005.
13. P. Dayan and L.F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, 2001.
14. S.R. Deiss, R.J. Douglas, and A.M. Whatley. A pulse-coded communications infrastructure for neuromorphic systems. In W. Maass and C.M. Bishop, editors, Pulsed Neural Networks, chapter 6, pages 157–78. MIT Press, 1998.
15. A. Destexhe, Z.F. Mainen, and T.J. Sejnowski. Methods in Neuronal Modelling, from ions to networks, chapter Kinetic Models of Synaptic Transmission, pages 1–25. The MIT Press, Cambridge, Massachussets, 1998.
16. M. Djurfeldt, M. Lundqvist, C. Johansson, M. Rehn, Ö. Ekeberg, and A. Lansner. Brain-scale simulation of the neocortex on the ibm blue gene/l supercomputer. IBM J. Res. Dev., 52(1/2):31–41, 2008.
17. R.J. Douglas, M.A. Mahowald, and K.A.C. Martin. Hybrid analog-digital architectures for neuromorphic systems. In Proc. IEEE World Congress on Computational Intelligence, volume 3, pages 1848–1853. IEEE, 1994.
18. R.J. Douglas and K.A.C. Martin. Neural circuits of the neocortex. Annual Review of Neuroscience, 27:419–51, 2004.
19. D.B. Fasnacht, A.M. Whatley, and G. Indiveri. A serial communication infrastructure for multi-chip address event system. In International Symposium on Circuits and Systems, ISCAS 2008, pages 648–651. IEEE, May 2008.
20. R. Freidman. Reverse engineering the brain. Biomedical Computation Review, 5(2):10–17, 2009.
21. S. Fusi, M. Annunziato, D. Badoni, A. Salamon, and D.J. Amit. Spike–driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Computation, 12:2227–58, 2000.
22. S. Fusi and W. Senn. Eluding oblivion with smart stochastic selection of synaptic updates. Chaos, An Interdisciplinary Journal of Nonlinear Science, 16(026112):1–11, 2006.
23. R. Hahnloser, R.J. Douglas, M.A. Mahowald, and K. Hepp. Feedback interactions between neuronal pointers and maps for attentional processing. Nature Neuroscience, 2:746–752, 1999.
24. R. Hahnloser, R. Sarpeshkar, M.A. Mahowald, R.J. Douglas, and S. Seung. Digital selection and analog amplification co-exist in an electronic circuit inspired by neocortex. Nature, 405(6789):947–951, 2000.
25. D. Hansel and H. Sompolinsky. Methods in Neuronal Modeling, chapter Modeling Feature Selectivity in Local Cortical Circuits, pages 499–567. MIT Press, Cambridge, Massachusetts, 1998.
26. A. Bofill i Petit and A.F. Murray. Learning temporal correlations in biologically-inspired aVLSI. In International Symposium on Circuits and Systems, ISCAS 2003, volume V, pages 817–820. IEEE, 2003.
27. G. Indiveri, E. Chicca, and R.J. Douglas. A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity. IEEE Transactions on Neural Networks, 17(1):211–221, Jan 2006.
28. G. Indiveri, B. Linares-Barranco, T.J. Hamilton, A. van Schaik, R. Etienne-Cummings, T. Delbruck, S.-C. Liu, P. Dudek, P. Häfliger, S. Renaud, J. Schemmel, G. Cauwenberghs, J. Arthur, K. Hynna, F. Folowosele, S. Saighi, T. Serrano-Gotarredona, J. Wijekoon, Y. Wang, and K. Boahen. Neuromorphic silicon neuron circuits. Frontiers in Neuroscience, 5:1–23, 2011.
29. E. Izhikevich and G. Edelman. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Science, 105:3593–3598, 2008.
30. K.R. Lakshmikumar, R.A. Hadaway, and M.A. Copeland. Characterization and modeling of mismatch in MOS transistors for precision analog design. IEEE Journal of Solid-State Circuits, SC-21(6):1057–1066, December 1986.
31. J. Lazzaro, S. Ryckebusch, M.A. Mahowald, and C.A. Mead. Winner-take-all networks of O(n) complexity. In D.S. Touretzky, editor, Advances in neural information processing systems, volume 2, pages 703–711, San Mateo - CA, 1989. Morgan Kaufmann.
32. J. Lazzaro, J. Wawrzynek, M. Mahowald, M. Sivilotti, and D. Gillespie. Silicon auditory processors as computer peripherals. IEEE Transactions on Neural Networks, 4:523–528, 1993.
33. U. Mallik, M. Clapp, E. Choi, G. Cauwenberghs, and R. Etienne-Cummings. Temporal change threshold detection imager. In ISSCC Dig. of Tech.Papers, pages 362–363, San Francisco, 2005.
34. Henry Markram. The blue brain project. In ACM/IEEE conference on Supercomputing, SC 2006, page 53, New York, NY, USA, 2006. IEEE, ACM.
35. R. Mill, S. Sheik, G. Indiveri, and S. Denham. A model of stimulus-specific adaptation in neuromorphic analog VLSI. IEEE Transactions on Biomedical Circuits and Systems, 2011. (submitted).
36. S. Mitra, S. Fusi, and G. Indiveri. Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Transactions on Biomedical Circuits and Systems, 3(1):32–42, Feb. 2009.
37. E. Neftci, E. Chicca, M. Cook, G. Indiveri, and R.J. Douglas. State-dependent sensory processing in networks of vlsi spiking neurons. In International Symposium on Circuits and Systems, ISCAS 2010, pages 2789–2792. IEEE, 2010.
38. E. Neftci and G. Indiveri. A device mismatch compensation method for VLSI spiking neural networks. In Biomedical Circuits and Systems Conference BIOCAS 2010, pages 262–265. IEEE, 2010.
39. H.K. Riis and P. Hafliger. Spike based learning with weak multi-level static memory. In International Symposium on Circuits and Systems, ISCAS 2004, pages 393–396. IEEE, 2004.
40. S. Sheik, F. Stefanini, E. Neftci, E. Chicca, and G. Indiveri. Systematic configuration and automatic tuning of neuromorphic systems. In International Symposium on Circuits and Systems, ISCAS 2011, pages 873–876. IEEE, May 2011.