Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 257-264

  Chicca, Elisabetta ^a   Indiveri, Giacomo ^a   Douglas, Rodney J ^a  

^a Institute of Neuroinformatics Zurich   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPETITIVE NETWORK; COMPUTATIONAL PROPERTIES; CONTEXT DEPENDENT; CORTICAL PROCESSING; EVENT CORRELATION; EXPERIMENTAL DATA; HIGHLY-CORRELATED; MEAN FIRING RATE; RECURRENT NETWORKS; SPIKE TIMING; SPIKING NEURON; VLSI IMPLEMENTATION;

IMAGE SEGMENTATION; NETWORK ARCHITECTURE; NEURONS;

NEURAL NETWORKS;

EID: 79952417495     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. R. J. Douglas and K. A. C. Martin. Neural circuits of the neocortex. Annual Review of Neuroscience, 27:419-51, 2004.
2. 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, 1994.
3. E. R. Kandel, J.H. Schwartz, and T. M. Jessell. Principles of Neural Science. Mc Graw Hill, 2000.
4. S. Amari and M. A. Arbib. Competition and cooperation in neural nets. In J. Metzler, editor, Systems Neuroscience, pages 119-65. Academic Press, 1977.
5. 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.
6. D. Hansel and H. Somplinsky. Methods in Neuronal Modeling, chapter Modeling Feature Selectivity in Local Cortical Circuits, pages 499-567. MIT Press, Cambridge, Massachusetts, 1998.
7. S. Amari. Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics, 27:77-87, 1977.
8. W. Erlhagen and G. Schöner. Dynamic field theory of movement preparation. Psychological Review, 109:545-572, 2002.
9. P. Dev. Perception of depth surfaces in random-dot stereograms: a neural model. International Journal of Man-Machine Studies, 7:511-28, 1975.
10. R. L. Didday. A model of visuomotor mechanisms in the frog optic tectum. Mathematical Biosciences, 30:169-80, 1976.
11. W. L. Kilmer, W. S. McCulloch, and J. Blum. A model of the vertebrate central command system. International Journal of Man-Machine Studies, 1:279-309, 1969.
12. R. Hahnloser, R. Sarpeshkar, M. 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.
13. R. Serrano-Gotarredona, M. Oster, P. Lichtsteiner, A. Linares-Barranco, R. Paz-Vicente, F. Gómez-Rodríguez, H. Kolle Riis, T. Delbrück, S. C. Liu, S. Zahnd, A. M. Whatley, R. J. Douglas, P. Häfliger, G. Jimenez-Moreno, A. Civit, T. Serrano-Gotarredona, A. Acosta- Jiménez, and B. Linares-Barranco. AER building blocks for multi-layer multi-chip neuromorphic vision systems. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15. MIT Press, Dec 2005.
14. J.P. Abrahamsen, P. Hafliger, and T.S. Lande. A time domain winner-take-all network of integrate-and-fire neurons. In 2004 IEEE International Symposium on Circuits and Systems, volume 5, pages V-361 - V-364, May 2004.
15. E. Chicca, G. Indiveri, and R. J. Douglas. An event based VLSI network of integrate-and-fire neurons. In Proceedings of IEEE International Symposium on Circuits and Systems, pages V-357-V-360. IEEE, 2004.
16. M. R. DeYong, R. L. Findley, and C. Fields. The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element. IEEE Transactions on Neural Networks, 3(3):363-74, 1992.
17. P. Hylander, J. Meador, and E. Frie. VLSI implementaion of pulse coded winner take all networks. In Proceedings of the 36th Midwest Symposium on Circuits and Systems, volume 1, pages 758-761, 1993.
18. G. Indiveri, R. Mürer, and J. Kramer. Active vision using an analog VLSI model of selective attention. IEEE Transactions on Circuits and Systems II, 48(5):492-500, May 2001.
19. M. Oster and S.-C. Liu. A winner-take-all spiking network with spiking inputs. In 11th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2004), 2004.
20. K. A. Boahen. Retinomorphic Vision Systems: Reverse Engineering the Vertebrate Retina. Ph.D. thesis, California Institute of Technology, Pasadena, CA, 1997.
21. E. Culurciello and A. G. Andreou. A comparative study of access topologies for chip-level address-event communication channels. IEEE Transactions on Neural Networks, 14(5):1266-77, September 2003.
22. E. Chicca, D. Badoni, V. Dante, M. D'Andreagiovanni, G. Salina, 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.
23. 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.
24. E. Chicca, A. M. Whatley, V. Dante, P. Lichtsteiner, 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, Regular Papers, 2006. (In press).
25. P. Dayan and F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, 2001.
26. E. Salinas and T. J. Sejnowski. Correlated neuronal activity and the flow of neural information. Nature Reviews Neuroscience, 2:539-550, 2001.