Using spatio-temporal correlations to learn invariant object recognition

Neural Networks

Volumn 9, Issue 9, 1996, Pages 1513-1519

  Wallis, Guy ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

character recognition; object recognition; self organizing neural network; time based Hebbian learning

Indexed keywords

CHARACTER RECOGNITION; CORRELATION METHODS; LEARNING SYSTEMS; NEURAL NETWORKS;

SELF ORGANIZING NEURAL NETWORK; TIME BASED HEBBIAN LEARNING;

OBJECT RECOGNITION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; LEARNING; PATTERN RECOGNITION; PRIORITY JOURNAL;

EID: 0030476783     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(96)00041-X     Document Type: Article

References (21)

1. Attneave, F. (1959). Applications of information theory to psychophysics: a summary of basic concepts, methods and results. New York: Holt, Rinehart and Winston.
2. Földiák P. Learning invariance from transformation sequences. Neural Computation. 3:1991;194-200.
3. Földiák, P. (1992). Models of sensory coding (Tech. rept. CUED/F-INFENG/TR 91). University of Cambridge, Department of Engineering.
4. Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics. 36:1980;193-202.
5. Fukushima K. Neocognitron: A hierarchical neural network model capable of visual pattern recognition unaffected by shift in position. Neural Networks. 1:1988;119-130.
6. Fukushima K. Character recognition with neural networks. Neurocomputing. 4(5):1992;221-233.
7. Goodale M.A., Milner A.D. Separate visual pathways for perception and action. Trends in Neurosciences. 15:1992;20-25.
8. Hecht-Nielsen, R. (1990). Neuralcomputing. Reading, MA: Addison-Wesley.
9. Hertz, J., Krogh, A., & Palmer, R.G. (1990). Introduction to the theory of neural computation. Santa Fe Institute: Addison Wesley.
10. Klopf A.H. A neuronal model of classical conditioning. Psychobiology. 16:1988;85-125.
11. LeCun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W., Jackel L.D. Backpropagation applied to handwritten zip code recognition. Neural Computation. 1:1989;541-551.
12. Lovell, D.R., & Tsoi, A.C. (1992). The performance of the Neocognitron with various S-cell and C-cell transfer functions. Paper posted to the "Connectionists" internet mail group. Produced in the Dept. of Electrical Engineering, University of Queensland, Queensland 4072, Australia.
13. Miyashita Y. Neuronal correlate of visual associtive long-term memory in the primate temporal cortex. Nature. 335:1988;817-820.
14. Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel distributed processing, Vol. 1: Foundations, Chap. 8. Cambridge, MA: MIT Press.
15. Rolls E.T. Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical areas. Philosophical Transactions of the Road Society, London [B], 335:1992;11-21.
16. Sutton R.S., Barto A.G. Towards a modern theory of adaptive networks: expectation and prediction. Psychological Review. 88:1981;135-170.
17. Tanaka K., Saito H., Fukada Y., Moriya M. Coding visual images of objects in the inferotemporal cortex of the macaque monkey. Journal of Neurophysiology. 66:1991;170-189.
18. Wallis, G. (1994). Neural mechanisms underlying processing in the visual areas of the occipital and temporal lobes. PhD thesis, Department of Experimental Psychology, Oxford University.
19. Wallis, G., & Rolls, E.G. (1995). A model of invariant object recognition in the visual system. Submitted to Journal of Computational Neuroscience.
20. Wallis G., Rolls E.T., Födiák P. Learning invariant responses to the natural transformations of objects. International Joint Conference on Neural Networks. 2:1993;1087-1090.
21. Widrow, B., & Hoff, M.E. (1960). Adaptive switching circuits. 1960 IRE WESCON Convention Record (Vol. 4, pp. 96-104) New York: Institute of Radio Engineers.