Classifying patterns of visual Motion - A neuromorphic approach

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Heinzle, Jakob ^a   Stocker, Alan ^a  

^a Institute of Neuroinformatics Zurich   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; NETWORK ARCHITECTURE; VISION;

CLASSIFICATION TASKS; CONTINUOUS MOTIONS; MAN-MACHINE INTERFACE; MOBILE ELECTRONICS; NETWORK STRUCTURES; NEUROMORPHIC APPROACH; SYSTEM-ON-CHIP IMPLEMENTATION; WINNER TAKE ALLS;

OPTICAL FLOWS;

EID: 84898958781     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. A. Stocker and R. J. Douglas. Computation of smooth optical fbw in a feedback connected analog network. Advances in Neural Information Processing Systems, 11: 706-712, 1999.
2. L. G. Sotelino, M. Saerens, and H. Bersini. Classification of temporal trajectories by continuous-time recurrent nets. Neural Networks, 7(5): 767-776, 1994.
3. D. T. Lin, J. E. Dayhoff, and P. A. Ligomenides. Trajectory recognition with a time-delay neural network. International Joint Conference on Neural Networks, Baltimore, III: 197-202, 1992.
4. H. Gutfreund and M. Mezard. Processing of temporal sequences in neural networks. Phys. Rev. Letters, 61(2): 235-238, July 1988.
5. D.-L. Lee. Pattern sequence recognition using a time-varying hopfield network. IEEE Trans. on Neural Networks, 13(2): 330-342, March 2002.
6. D. W. Tank and J. J. Hopfield. Neural computation by concentrating information in time. Proc. Natl. Acad. Sci. USA, 84: 1896-1900, April 1987.
7. J. J. Hopfield and C. D. Brody. What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc. Natl. Acad. Sci. USA, 98: 1282-1287, January 2001.
8. A. Stocker. Constraint optimization networks for visual motion perception - analysis and synthesis. PhD thesis, ETH Zürich, No. 14360, 2001.
9. J. J. Hopfield. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA, 81: 3088-3092, May 1984.
10. R. Hahnloser, R. Sarpeshkar, M. Mahowald, R. Douglas, and S. Seung. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature, 405: 947-951, June 2000.