A real-time, event-driven neuromorphic system for goal-directed attentional selection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7664 LNCS, Issue PART 2, 2012, Pages 226-233

  Galluppi, Francesco ^a   Brohan, Kevin ^a   Davidson, Simon ^a   Serrano Gotarredona, Teresa ^b   Carrasco, José Antonio Pérez ^c   Linares Barranco, Bernabé ^b   Furber, Steve ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b INSTITUTO DE MICROELECTRÓNICA DE SEVILLA   (Spain)

^c UNIVERSITY OF SEVILLE   (Spain)

Author keywords

AER; Attention; Neuromorphic; Selection; SpiNNaker

Indexed keywords

AER; ATTENTION; NEUROMORPHIC; SELECTION; SPINNAKER;

DATA PROCESSING; ELECTROPHYSIOLOGY;

NEURAL NETWORKS;

EID: 84869047830     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34481-7_28     Document Type: Conference Paper

References (20)

1. Knudsen, E.: Fundamental components of attention. Annu. Rev. Neurosci. 30, 57-78 (2007)
2. Van Rullen, R., Thorpe, S.J.: Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput. 13(6), 1255-1283 (2001)
3. Lichtsteiner, P., Posch, C., Delbruck, T.: A 128x128 120dB 15us Latency Asynchronous Temporal Contrast Vision Sensor. IEEE J. Solid State Circuits 43, 566-576 (2008)
4. Lenero-Bardallo, J.A., Serrano-Gotarredona, T., Linares-Barranco, B.: A 3.6 us Latency Asynchronous Frame-Free Event-Driven Dynamic-Vision-Sensor. IEEE Journal of Solid State Circuits 46(6), 1443-1455 (2011)
5. Sonnleithner, D., Indiveri, G.: A Neuromorphic Saliency-Map based Active Vision System. Neuroinformatics, 1-6 (2011)
6. Indiveri, G., Linares-Barranco, B., Hamilton, T.J., Van Schaik, A., Etienne- Cummings, R., Delbruck, T., Liu, S.C., Dudek, P., Häfliger, P., Renaud, S., Schemmel, J., Cauwenberghs, G., Arthur, J., Hynna, K., Folowosele, F., Saighi, S., Serrano-Gotarredona, T., Wijekoon, J., Wang, Y., Boahen, K.: Neuromorphic Silicon Neuron Circuits. Front. Neurosci. 5 (2011)
7. Wijekoon, J.H.B., Dudek, P.: VLSI circuits implementing computational models of neocortical circuits. J. Neurosci. Meth. (2012)
8. Furber, S., Brown, A.: Biologically-Inspired Massively-Parallel Architectures - Computing Beyond a Million Processors. In: 2009 Ninth International Conference on Application of Concurrency to System Design, ACSD 2009, pp. 3-12. IEEE, Washington, DC (2009)
9. Serrano-Gotarredona, R., Oster, M., Lichtsteiner, P., Linares-Barranco, A., Paz-Vicente, R., Gomez-Rodriguez, F., Camunas-Mesa, L., Berner, R., Rivas-Perez, M., Delbruck, T., Liu, S.C., Douglas, R., Hafliger, P., Jimenez-Moreno, G., Civit Ballcels, A., Serrano-Gotarredona, T., Acosta-Jimenez, A.J., Linares-Barranco, B.: CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensoryprocessing-learning-actuating system for high-speed visual object recognition and tracking. IEEE Trans. Neural. Netw. 20(9), 1417-1438 (2009)
10. Davies, S., Patterson, C., Galluppi, F.: Interfacing Real-Time Spiking I/O with the SpiNNaker neuromimetic architecture. AJIIPS 11(1) (2010)
11. Davison, A.P., Bruderle, D., Eppler, J., Kremkow, J., Muller, E., Pecevski, D., Perrinet, L., Yger, P.: PyNN: A Common Interface for Neuronal Network Simulators. Front. Neuroinform. 2 (2008)
12. Galluppi, F., Davies, S., Rast, A., Sharp, T., Plana, L., Furber, S.: A Hierarchical Configuration System for a Massively Parallel Neural Hardware Platform. In: ACM (ed.) CF 2012 Proceedings of the 9th Conference on Computing Frontiers, Cagliari, pp. 183-192 (2012)
13. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE T. Pattern. Anal. 20(11) (1998)
14. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019-1025 (1999)
15. Duncan, J., Humphreys, G.W.: Visual search and stimulus similarity. Psychol. Rev. 96(3), 433-458 (1989)
16. Plana, L., Furber, S., Temple, S., Khan, M., Shi, Y., Wu, J., Yang, S.: A GALS Infrastructure for a Massively Parallel Multiprocessor. IEEE Design & Test of Computers 24(5), 454-463 (2007)
17. Brohan, K., Gurney, K., Dudek, P.: Using Reinforcement Learning to Guide the Development of Self-organised Feature Maps for Visual Orienting. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part II. LNCS, vol. 6353, pp. 180-189. Springer, Heidelberg (2010)
18. Hubel, D.H.: Eye, Brain, and Vision. Scientific American Library, New York (1988)
19. Noudoost, B., Chang, M.H., Steinmetz, N.A., Moore, T.: Top-down control of visual attention. Current Opinion in Neurobiology 20(2), 183-190 (2010)
20. Bisley, J.W., Mirpour, K., Arcizet, F., Ong, W.S.: The role of the lateral intraparietal area in orienting attention and its implications for visual search. Eur. J. Neurosci. 33, 1982-1990 (2011)