Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Giulioni, Massimiliano ^a   Corradi, Federico ^a,b   Dante, Vittorio ^a   Del Giudice, Paolo ^a,c  

^a ISTITUTO SUPERIORE DI SANITÀ   (Italy)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c INFN SEZIONE DI ROMA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE MEMORY; INFORMATION RETRIEVAL; LEARNING; LEISURE; NERVE CELL PLASTICITY; STIMULUS; ANIMAL; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOMIMETICS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SYSTEM; DEVICE FAILURE ANALYSIS; DEVICES; EQUIPMENT DESIGN; HUMAN; PHOTOSTIMULATION; PHYSIOLOGY; PROCEDURES; SIGNAL PROCESSING; UNSUPERVISED MACHINE LEARNING; VISION;

ANIMALS; BIOMIMETICS; COMPUTER SYSTEMS; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PHOTIC STIMULATION; SIGNAL PROCESSING, COMPUTER-ASSISTED; UNSUPERVISED MACHINE LEARNING; VISUAL PERCEPTION;

EID: 84944342778     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep14730     Document Type: Article

References (53)

1. Mead, C. Analog VLSI Implementation of Neural Systems. (Springer, 1989).
2. Cordeschi, R. The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. (Kluwer Academic Publisher, The Netherlands, 2002).
3. Liu, S. C., Van Schaik, A., Minch, B. A. & Delbruck, T. Asynchronous binaural spatial audition sensor with 2x64x4 channel output. IEEE Trans. on Biomed. Circ. and Syst. 99, 1-12 (2013).
4. Liu, S. C. & Delbruck, T. Neuromorphic Sensory Systems. Curr. Opin. in Neurobio. 20, 1-8, (Martin K. & Scott K. ed.) (2010).
5. Liu, S. C., Delbruck, T., Indiveri, G., Whatley, A. & Douglas, R. Event-Based Neuromorphic Systems (Wiley, 2015).
6. Neftci, E. et al. Synthesizing cognition in neuromorphic electronic systems. Proc. Nat. Acad. Sci., PNAS. 110, E3468-E3476 (2012).
7. Chicca E., Stefanini, F., Bartolozzi, C. & Indiveri, G. Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems. Proc. of the IEEE. 102, 1-22 (2014).
8. Amit, D. J. Modeling Brain Function: The World of Attractor Neural Networks (Cambridge University Press, 1989).
9. Wang, X.-J. Attractor Network Models, Encyclopedia of Neuroscience. (eds Squire, L. R.), 667-679 (Oxford Academic Press, 2008).
10. Giulioni, M. et al. Robust working memory in an asynchronously spiking neural network realized in neuromorphic VLSI. Front. in Neurosc. 5, doi: 10.3389/fnins.2011.00149 (2012).
11. Mongillo, G., Curti, E., Romani, S. & Amit, D. J. Learning in realistic networks of spiking neurons and spike-driven plastic synapses. Eur. J. Neurosc. 21, 3143-3160 (2005).
12. Amit, D. J. & Brunel, N. Learning internal representations in an attractor neural network with analogue neurons. Network: Computation in Neural Systems 6, 359-388 (1995).
13. Del Giudice, P., Fusi, S. & Mattia, M. Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses. J. Physiol. Paris 97, 659-681 (2003).
14. Del Giudice, P. & Mattia, M. Long and short-term synaptic plasticity and the formation of working memory: A case study. Neurocomp. 38, 1175-1180 (2001).
15. Lichtsteiner, P., Posch, C. & Delbruck, T. A 128x128, 120 dB 15 micro s latency asynchronous temporal contrast vision sensor. IEEE Jour. of Solid St. Circ. 43, 566-576 (2008).
16. Giulioni, M. et al. A VLSI network of spiking neurons with plastic fully configurable stop-learning synapses. Inter. Conf. on Elec., Circ., and Syst., ICECS. 678-681; doi: 10.1109/ICECS.2008.4674944 (2008).
17. Giulioni, M., Pannunzi, M., Badoni, D., Dante, V. & Del Giudice, P. Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses. Neural Comput. 21, 3106-3129 (2009).
18. Fusi, S., Annunziato, M., Badoni, D., Salamon, A. & Amit, D. J. Spike-driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Comput. 12, 2227-2258 (2000).
19. Mahowald, M. An analog VLSI system for stereoscopic vision (Springer, 1994).
20. Dante, V., Del Giudice, P. & Whatley, A. M. PCI-AER - hardware and software for interfacing to address-event based neuromorphic systems. The Neuromorphic Engineer 2, 5-6 (2005).
21. Mascaro, M. & Amit, D. J. Effective neural response function for collective population states. Network: Computation in Neural Systems. 10, 351-373 (1999).
22. Fusi, S. Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cyber. 87, 459-470 (2002).
23. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci., (PNAS) USA 79, 2554-2558 (1982).
24. Curti, E., Mongillo, G., La Camera, G. & Amit, D. J. Mean field and capacity in realistic networks of spiking neurons storing sparsely coded random memories. Neural Comput. 16, 2597-2637 (2004).
25. Fusi, S. & Abbott, L. Limits on the memory storage capacity of bounded synapses. Nature Neuroscience. 10, 485-493 (2007).
26. Neftci, E. & Indiveri, G. A device mismatch compensation method for VLSI neural networks. IEEE Biom. Circ. and Sys. Conf. (BioCAS). 262-265; doi: 10.1109/BIOCAS.2010.5709621 (2010).
27. Brader, J. M., Senn, W. & Fusi, S. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Comput. 19, 2881-2912 (2007).
28. Mitra, S., Fusi, S. & Indiveri, G. Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Trans. on Biom. Circ. and Syst. 3, 32-42 (2009).
29. Widrow, B. & Hoff, M. E. Adaptive switching circuits. Neurocomputing: foundations of research. (The MIT press, Cambridge, USA, 1988).
30. Alspector, J., Gupta, B. & Allen, R. B. Performance of a stochastic learning microchip. Advances in neural information processing systems. Touretzky, D. S. (ed.) 748-760 (Morgan-Kaufmann, 1989).
31. Cauwenberghs, G. Neuromorphic Learning VLSI Systems: a Survey (eds Sejnowski, T. J.) 381-408 (Springer, 1998).
32. Hafliger, P. Adaptive WTA with an analog VLSI neuromorphic learning chip. IEEE Trans. on Neural Networks. 18, 551-572 (2007).
33. Serrano-Gotarredona, R. et al. CAVIAR: A 45k neuron, 5M synapse, 12G connects/s AER hardware sensory processing learning actuating system for high-speed visual object recognition and tracking. IEEE Trans. on Neural Networks. 20, 1417-1438 (2009).
34. Hafliger, P. & Kolle Riis, H. A multi-level static memory cell. IEEE Int. Symp. on Circ. and Sys. (ISCAS). 1, 25-28 (2003).
35. Gordon, C. & Hasler, P. Biological learning modeled in an adaptive floating-gate system. IEEE Int. Symp. on Circ. and Sys. 5, 609-612 (2002).
36. Liu, S. C. & Mockel, R. Temporally learning floating-gate VLSI synapses. IEEE Int. Symp. on Circ. and Sys. (ISCAS). 2154-2157, doi: 10.1109/ISCAS.2008.4541877 (2008).
37. Ramakrishnan, S., Hasler, P. E. & Gordon, C. Floating gate synapses with spike-time-dependent plasticity. IEEE Trans. on Biom. Circ. and Sys. (BIOCAS). 5, 244-252 (2011).
38. Bofill-I-Petit, A. & Murray, A. F., Synchrony detection and amplification by silicon neurons with STDP synapses. IEEE Trans. on Neural Networks. 15, 1296-1304 (2004).
39. Schemmel, J., Grubl, A., Meier, K. & Mueller, E. Implementing synaptic plasticity in a VLSI spiking neural network model. IEEE Int. Joint Conf. on Neural Networks (IJCNN). 1-6, doi: 10.1109/IJCNN.2006.246651 (2006).
40. Bamford, S. A., Murray, A. F. & Willshaw, D. J. Spike-timing-dependent plasticity with weight dependence evoked from physical constraints. IEEE Trans. on Biom. Circ. and Sys. 6, 385-398 (2012).
41. Rachmuth, G., Shouval, H. Z., Bear, M. F. & Poon, C. S. A biophysically-based neuromorphic model of spike rate-and timingdependent plasticity. Proc. Nat. Acad. Sci., (PNAS) 108, E1266-E1274 (2011).
42. Indiveri, G., Chicca, E. & Douglas, R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. on Neural Network. 17, 211-221 (2006).
43. Indiveri, G., Stefanini, F. & Chicca, E. Spike-based learning with a generalized integrate and fire silicon neuron. IEEE Int. Symp. on Circ. and Sys., (ISCAS), 1951-1954, doi: 10.1109/ISCAS.2010.5536980 (2010).
44. Brink, S. et al. A learning-enabled neuron array is based upon transistor channel models of biological phenomena. IEEE Trans. on Biomed. Circ. and Syst. 7, 71-81 (2013).
45. Suri, M. et al. Physical aspects of low power synapses based on phase change memory devices. J. of Appl. Phys. 112, 054904 (2012).
46. Mayr, C. et al. Waveform driven plasticity in BiFeO3 memristive devices: model and implementation. Adv. in Neur. Infor. Proc. Syst., (NIPS). 25, 2323-2326 (2012).
47. Serrano-Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G. & Linares-Barranco, B. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front. in Neurosc. 7, doi: 10.3389/fnins.2013.00002 (2013).
48. Gelencser, A., Prodromakis, T., Toumazou, C. & Roska, T. Biomimetic model of the outer plexiform layer by incorporating memristive devices. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 85, 041918 (2012).
49. Kuzum, D., Yu, S. & Wong, H. P. Synaptic electronics: materials, devices and applications. Nanotechnology. 24, 382001 (2013).
50. Arthur, J. & Boahen, K. Learning in silicon: timing is everything. Advances in Neural Information Processing Systems. 75-82 (The MIT Press, USA, 2006).
51. Seo, J. et al. A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons. IEEE Cust. Integr. Circ. Conf., CICC (Piscataway, NJ). 1-4, doi: 10.1109/CICC.2011.6055293 (2001).
52. Braun, J. & Mattia, M. Attractors end noise: twin drivers of decisions and multistability. Neuroimage 52, 740-751 (2010).
53. Mattia, M. & Del Giudice, P. Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. Phys. Rev. E Stat. Nonlin. Soft. Matter Phys. 70, 052903 (2004).