Modeling the adaptive visual system: A survey of principled approaches

Neural Networks

Volumn 16, Issue 9, 2003, Pages 1353-1371

  Schwabe, Lars ^a   Obermayer, Klaus ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

Bayesian inference; Infomax; MDL principle; Models; Visual cortex

Indexed keywords

ADAPTIVE SYSTEMS; CALCULATIONS; COMPUTER SIMULATION;

TRANSIENT NEURONAL DYNAMICS;

NEURAL NETWORKS;

BAYES THEOREM; HUMAN; MODEL; NERVE CELL; NONHUMAN; PRIMATE; PRIORITY JOURNAL; RECEPTIVE FIELD; REVIEW; STIMULUS RESPONSE; VISUAL CORTEX; VISUAL STIMULATION; VISUAL SYSTEM;

EID: 0242551606     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2003.07.009     Document Type: Article

References (35)

1. Adorjan P., Levitt J.B., Lund J.S., Obermayer K. A model for the intracortical origin of orientation preference and tuning in macaque striate cortex. Visual Neuroscience. 16:1999;303-318.
2. Adorjan P., Piepenbrock C., Obermayer K. Contrast adaptation and infomax in visual cortical neurons. Reviews in the Neurosciences. 10:1999;181-200.
3. Adorján P., Schwabe L., Piepenbrock C., Obermayer K. Recurrent current competition: strengthen or weaken. Solla S.A., Leen T.K., Müller K.-R. Neural Information Processing Systems NIPS. Vol. 12:2000;89-95 MIT Press.
4. Adorjan, P., Schwabe, L., Wenning, G., Obermayer, K (2002). Rapid adaptation to internal states as a coding strategy in visual cortex? Neuroreport, 13(3), 337-42.
5. Barlow H.B. Redundancy reduction revisited. Network. 7:(2):2001;251-259.
6. Becker S., Hinton G. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature. 355:1992;161-163.
7. Berkes P., Wiskott L. Applying slow feature analysis to image sequences yields a rich repertoire of complex cell Properties. In Proceedings of the ICANN'02. 2002;Springer. pp. 81-86.
8. Brenner N., Bialek W., de Ruyter R., de Ruyter van Steveninck R. Adaptive rescaling maximizes information transmission. Neuron. 23:(3):2000;695-702.
9. Carandini M., Ferster D. A tonic hyperpolarization underlying contrast adaptation in cat visual cortex. Science. 276:1997;949-952.
10. Cover T., Thomas J. Elements of information theory. 1991;Wiley, New York.
11. Ferster D., Miller K.D. Neural mechanisms of orientation selectivity in the visual cortex. Annual Reviews of Neuroscience. 2000;23.
12. Grossberg S. Adaptive pattern classification and universal recoding, I: parallel development and coding of neural feature detectors. Biological Cybernetics. 23:1976;121-134.
13. Hubel D.H., Wiesel T.N. Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology. 195:1968;215-243.
14. Hubel D.H., Wiesel T.N. Functional architecture of macaque monkey visual cortex. Proceedings of Royal Society of London B. 198:1977;1-59.
15. Kaelbling L.P., Littman M.L., Moore A.W. Reinforcement learning: a survey. Journal of Artificial Intelligance Research. 4:1996;237-286.
16. Kandel E.R., Schwarz J.H., Jesse T.M. Principles of neural science. 1996;McGraw-Hill, New York.
17. Kersten D., Schrater P.W. Perception theory: Conceptual pattern inference theory: A probabilistic approach to human vision. 1999;Wiley, New York.
18. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature. 401:1999;759-760.
19. Levitt J.B., Lund J.S. Contrast dependence of contextual effects in primate visual cortex. Nature. 387:1997;73-76.
20. Marr D. Vision. 1982;Freeman, New York.
21. Minsky M. Computation: Finite and infinite machines. 1967;Prentice Hall, Engelwood Cliffs, NJ.
22. Olshausen B.A., Field D.J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 381:1996;607-609.
23. Poggio T., Torre V., Koch C. Computational vision and regularization theory. Nature. 317:1985;314-319.
24. Pouget A., Dayan P., Zemel R. Information processing with population codes. Nature Review in Neurosciences. 12:2000;125-132.
25. Rao R.P.N., Ballard D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience. 1999;2.
26. Rissanen J. Stochastic complexity in statistical inquiry. 1989;World Scientific Publishing, Singapore.
27. Schwabe L., Adorjan P., Obermayer K. Spike-frequency adaptation as a mechanism for dynamic coding. Neurocomputing. 38-40:2001;351-358.
28. Seitz A.R., Watanabe T. Is subliminal learning really passive? Nature. 422:(6927):2003;36.
29. Siegelmann H.T., Sontag E.D. Turing computability with neural nets. Applied Mathematics Letters. 4:1991;77-80.
30. Sompolinsky H., Shapley R. New perspectives on the mechanisms for orientation selectivity. Current Opinion in Neurobiology. 7:1997;514-522.
31. Ullman S., Vidal-Naquet M., Sali E. Visual features of intermediate complexity and their use in classification. Nature Neuroscience. 2002;5.
32. van Vreeswijk C. Using renewal neurons to transmit information. European Biophysics Journal. 29:2000;245.
33. van Vreeswijk, C (2000b). Whence Sparseness. In: Neural Information Processing Systems NIPS, MIT Press (vol. 13), pp. 180-186.
34. Wiskott L., Sejnowski T.J. Slow feature analysis: unsupervised learning of invariances. Neural Computation. 14:(4):2002;715-770.
35. Zemel R., Dayan P., Pouget A. Probabilistic interpretation of population codes. Neural Computation. 1998.