Cogprints
Login | Create Account

Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex

Bednar, James A. and Miikkulainen, Risto (2000) Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex. [Journal (Paginated)]

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
159Kb
[img]Postscript - Requires a viewer, such as GSview
183Kb

Abstract

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short time scales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex.

Item Type:Journal (Paginated)
Keywords:tilt aftereffect, tilt after-effect, tilt aftereffects, tilt after-effects, TAE, tilt illusions, TI, orientation aftereffects, orientation after-effects, orientation illusions, visual illusions, aftereffects, illusions, primary visual cortex, self-organization, orientation maps, neural networks, perception, vision, visual development, visual function, computational neuroscience, normalization, synapses, short-term plasticity, resource redistribution, neuronal regulation, homeostasis, direct effect, indirect effect, adaptation, psychophysics
Subjects:Neuroscience > Computational Neuroscience
Computer Science > Neural Nets
Neuroscience > Neural Modelling
Psychology > Psychophysics
ID Code:1915
Deposited By:Bednar, James A.
Deposited On:23 Nov 2001
Last Modified:12 Sep 2007 17:41

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Anderson, J. A., & Rosenfeld, E. (Eds.) (1988). Neurocomputing: Foundations of Research. Cambridge, MA: MIT Press.

Barlow, H. B. (1990). A theory about the functional role and synaptic mechanism of visual aftereffects. In Blakemore, C. (Ed.), Vision: Coding and Efficiency (pp. 363-375). New York: Cambridge University Press.

Bednar, J. A. (1997). Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex. Master's thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI97-259.

Bednar, J. A., & Miikkulainen, R. (1998). Pattern-generator-driven development in self-organizing models. In Bower, J. M. (Ed.), Computational Neuroscience: Trends in Research, 1998 (pp. 317-323). New York: Plenum.

Bosking, W. H., Zhang, Y., Schofield, B., & Fitzpatrick, D. (1997). Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. Journal of Neuroscience, 17 (6), 2112-2127.

Campbell, F. W., & Maffei, L. (1971). The tilt aftereffect: A fresh look. Vision Research, 11, 833-840.

Carpenter, R. H. S., & Blakemore, C. (1973). Interactions between orientations in human vision. Experimental Brain Research, 18, 287-303.

Choe, Y., & Miikkulainen, R. (1998). Self-organization and segmentation in a laterally connected orientation map of spiking neurons. Neurocomputing, 21, 139-157.

Coltheart, M. (1971). Visual feature-analyzers and aftereffects of tilt and curvature. Psychological Review, 78 (2), 114-121.

Dong, D. W. (1995). Associative decorrelation dynamics: A theory of self-organization and optimization in feedback networks. In Tesauro, G., Touretzky, D. S., & Leen, T. K. (Eds.), Advances in Neural Information Processing Systems 7 (pp. 925-932). Cambridge, MA: MIT Press.

Field, D. J. (1994). What is the goal of sensory coding? Neural Computation, 6, 559-601.

Finlayson, P. G., & Cynader, M. S. (1995). Synaptic depression in visual cortex tissue slices: An in vitro model for cortical neuron adaptation. Experimental Brain Research, 106 (1), 145-155.

Fo"ldiak, P. (1990). Forming sparse representations by local anti-Hebbian learning. Biological Cybernetics, 64, 165-170.

Fregnac, Y. (1996). Dynamics of functional connectivity in visual cortical networks: An overview. Journal of Physiology (Paris), 90, 113-139.

Geisler, W. S., & Albrecht, D. G. (1997). Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification. Visual Neuroscience, 14 (5), 897-919.

Gibson, J. J., & Radner, M. (1937). Adaptation, after-effect and contrast in the perception of tilted lines. Journal of Experimental Psychology, 20, 453-467.

Gilbert, C. D. (1998). Adult cortical dynamics. Physiological Reviews, 78 (2), 467-485.

Greenlee, M. W., & Magnussen, S. (1987). Saturation of the tilt aftereffect. Vision Research, 27 (6), 1041-1043.

Grinvald, A., Lieke, E. E., Frostig, R. D., & Hildesheim, R. (1994). Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex. Journal of Neuroscience, 14, 2545-2568.

Hata, Y., Tsumoto, T., Sato, H., Hagihara, K., & Tamura, H. (1993). Development of local horizontal interactions in cat visual cortex studied by cross-correlation analysis. Journal of Neurophysiology, 69, 40-56.

Hirsch, J. A., & Gilbert, C. D. (1991). Synaptic physiology of horizontal connections in the cat's visual cortex. Journal of Neuroscience, 11, 1800-1809.

Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195, 215-243.

Magnussen, S., & Johnsen, T. (1986). Temporal aspects of spatial adaptation: A study of the tilt aftereffect. Vision Research, 26 (4), 661-672.

McLean, J., & Palmer, L. A. (1996). Contrast adaptation and excitatory amino acid receptors in cat striate cortex. Visual Neuroscience, 13 (6), 1069-1087.

Miikkulainen, R., Bednar, J. A., Choe, Y., & Sirosh, J. (1997). Self-organization, plasticity, and low-level visual phenomena in a laterally connected map model of the primary visual cortex. In Goldstone, R. L., Schyns, P. G., & Medin, D. L. (Eds.), Perceptual Learning (Vol. 36 of Psychology of Learning and Motivation, pp. 257-308). San Diego, CA: Academic Press.

Miller, K. D., & MacKay, D. J. C. (1994). The role of constraints in Hebbian learning. Neural Computation, 6, 100-126.

Mitchell, D. E., & Muir, D. W. (1976). Does the tilt aftereffect occur in the oblique meridian? Vision Research, 16, 609-613.

Mundel, T., Dimitrov, A., & Cowan, J. D. (1997). Visual cortex circuitry and orientation tuning. In Mozer, M. C., Jordan, M. I., & Petsche, T. (Eds.), Advances in Neural Information Processing Systems 9 (pp. 887-893). Cambridge, MA: MIT Press.

Parker, A. J., & Newsome, W. T. (1998). Sense and the single neuron: Probing the physiology of perception. Annual Review of Neuroscience, 21, 227-277.

Rochester, N., Holland, J. H., Haibt, L. H., & Duda, W. L. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on Information Theory, 2, 80-93. Reprinted in Anderson & Rosenfeld, 1988.

Shatz, C. J. (1990). Impulse activity and the patterning of connections during CNS development. Neuron, 5, 745-756.

Sirosh, J. (1995). A Self-Organizing Neural Network Model of the Primary Visual Cortex. Doctoral Dissertation, Department of Computer Sciences, The University of Texas at Austin, Austin, TX. Technical Report AI95-237.

Sirosh, J., & Miikkulainen, R. (1994a). Cooperative self-organization of afferent and lateral connections in cortical maps. Biological Cybernetics, 71, 66-78.

Sirosh, J., & Miikkulainen, R. (1994b). Modeling cortical plasticity based on adapting lateral interaction. In Bower, J. M. (Ed.), The Neurobiology of Computation: The Proceedings of the Third Annual Computation and Neural Systems Conference (pp. 305-310). Dordrecht: Kluwer.

Sirosh, J., & Miikkulainen, R. (1997). Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural Computation, 9, 577-594.

Sirosh, J., Miikkulainen, R., & Bednar, J. A. (1996). Self-organization of orientation maps, lateral connections, and dynamic receptive fields in the primary visual cortex. In Sirosh, J., Miikkulainen, R., & Choe, Y. (Eds.), Lateral Interactions in the Cortex: Structure and Function. Austin, TX: The UTCS Neural Networks Research Group. Electronic book, ISBN 0-96470600-8, http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96.

Snippe, H. P. (1996). Parameter extraction from population codes: A critical assessment. Neural Computation, 8 (3), 511-529.

Stemmler, M., Usher, M., & Niebur, E. (1995). Lateral interactions in primary visual cortex: A model bridging physiology and psychophysics. Science, 269, 1877-1880.

Tolhurst, D. J., & Thompson, P. G. (1975). Orientation illusions and aftereffects: Inhibition between channels. Vision Research, 15, 967-972.

Turrigiano, G., Abbott, L. F., & Marder, E. (1994). Activity-dependent changes in the intrinsic properties of cultured neurons. Science, 264, 974-977.

Turrigiano, G. G. (1999). Homeostatic plasticity in neuronal networks: The more things change, the more they stay the same. Trends in Neurosciences, 22 (5), 221-227.

Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B. (1998). Activitydependent scaling of quantal amplitude in neocortical neurons. Nature, 391, 845-846.

van der Zwan, R., & Wenderoth, P. (1995). Mechanisms of purely subjective contour tilt aftereffects. Vision Research, 35 (18), 2547-2557.

Vidyasagar, T. R. (1990). Pattern adaptation in cat visual cortex is a co-operative phenomenon. Neuroscience, 36, 175-179.

von der Malsburg, C. (1973). Self-organization of orientation-sensitive cells in the striate cortex. Kybernetik, 15, 85-100. Reprinted in Anderson & Rosenfeld, 1988.

von der Malsburg, C. (1987). Synaptic plasticity as basis of brain organization. In Changeux, J.P., & Konishi, M. (Eds.), The Neural and Molecular Bases of Learning (pp. 411-432). New York: Wiley.

Weliky, M., Kandler, K., Fitzpatrick, D., & Katz, L. C. (1995). Patterns of excitation and inhibition evoked by horizontal connections in visual cortex share a common relationship to orientation columns. Neuron, 15, 541-552.

Wenderoth, P., & Johnstone, S. (1988). The different mechanisms of the direct and indirect tilt illusions. Vision Research, 28, 301-312.

Wolfe, J. M., & O'Connell, K. M. (1986). Fatigue and structural change: Two consequences of visual pattern adaptation. Investigative Ophthalmology and Visual Science, 27 (4), 538-543.

Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10 (2), 403-430.

Zucker, R. S. (1989). Short-term synaptic plasticity. Annual Review of Neuroscience, 12, 13-31.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.