Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-15T13:25:45.018Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficient coding correlates with spatial frequency tuning in a model of V1 receptive field organization

Published online by Cambridge University Press:  01 January 2009

JAN WILTSCHUT  and
FRED H. HAMKER
JAN WILTSCHUT
Affiliation:
Psychology and Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, Westf. Wilhelms-Universität Münster, Münster, Germany
FRED H. HAMKER*
Affiliation:
Psychology and Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, Westf. Wilhelms-Universität Münster, Münster, Germany
*
*Address correspondence and reprint requests to: Fred H. Hamker, Allgemeine Psychologie, Psychologisches Institut II, Westf. Wilhelms-Universität, Fliednerstrasse 21, 48149 Münster, Germany. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Efficient coding has been proposed to play an essential role in early visual processing. While several approaches used an objective function to optimize a particular aspect of efficient coding, such as the minimization of mutual information or the maximization of sparseness, we here explore how different estimates of efficient coding in a model with nonlinear dynamics and Hebbian learning determine the similarity of model receptive fields to V1 data with respect to spatial tuning. Our simulation results indicate that most measures of efficient coding correlate with the similarity of model receptive field data to V1 data, that is, optimizing the estimate of efficient coding increases the similarity of the model data to experimental data. However, the degree of the correlation varies with the different estimates of efficient coding, and in particular, the variance in the firing pattern of each cell does not predict a similarity of model and experimental data.

Keywords

Network modelsNatural scenesEfficient codingHebbian learningVisual system
Type
Natural Scene Statistics and Efficient Coding
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 21 - 34
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Atick, J.J. & Redlich, A.N. (1990). Towards a theory of early visual processing. Neural Computation 2, 308–320.CrossRefGoogle Scholar
Attneave, F. (1954). Some informational aspects of visual perception. Psychological Review 61, 183–193.CrossRefGoogle ScholarPubMed
Barlow, H.B. (1961). Possible principles underlying the transformation of sensory messages. In Sensory Communication, ed. Rosenblith, W.A., pp. 217–234. Cambridge, MA: MIT Press.Google Scholar
Barlow, H.B. (1989). Unsupervised learning. Neural Computation 1, 295–311.CrossRefGoogle Scholar
Bayerl, P. & Neumann, H. (2004). Disambiguating visual motion through contextual feedback modulation. Neural Computation 16, 2041–2066.CrossRefGoogle ScholarPubMed
Bell, A.J. & Sejnowski, T.J. (1997). The “independent components” of natural scenes are edge filters. Vision Research 37, 3327–3338.CrossRefGoogle Scholar
Bethge, M. (2006). Factorial coding of natural images: How effective are linear models in removing higher-order dependencies? Journal of the Optical Society of America. A, Optics, Image Science, and Vision 23, 1253–1268.CrossRefGoogle ScholarPubMed
Calow, D. & Lappe, M. (2007). Local statistics of retinal optic flow for self-motion through natural sceneries. Network 18, 343–374.CrossRefGoogle ScholarPubMed
Coleman, T. & Li, Y. (1994). On the convergence of reflective Newton methods for large-scale nonlinear minimization subject to bounds. Mathematical Programming 67, 189–224.CrossRefGoogle Scholar
Coleman, T. & Li, Y. (1996). An interior, trust region approach for nonlinear minimization subject to bounds. SIAM Journal on Optimization 6, 418–445.CrossRefGoogle Scholar
Daugman, J.G. (1989). Entropy reduction and decorrelation in visual coding by oriented neural receptive fields. IEEE Transactions on Biomedical Engineering 36, 107–114.CrossRefGoogle ScholarPubMed
DeAngelis, G.C., Ohzawa, I. & Freeman, R.D. (1993). Spatiotemporal organization of simple-cell receptive fields in the cat’s striate cortex. I. General characteristics and postnatal development. Journal of Neurophysiology 69, 1091–1117.CrossRefGoogle ScholarPubMed
Falconbridge, M.S., Stamps, R.L. & Badcock, D.R. (2006). A simple Hebbian/anti-Hebbian network learns the sparse, independent components of natural images. Neural Computation 18, 415–429.CrossRefGoogle ScholarPubMed
Field, D.J. (1984). A Space Domain Approach to Pattern Vision: An Investigation of Phase Discrimination and Masking. Ph.D. Thesis, University of Pennsylvania (unpublished).Google Scholar
Field, D.J. (1994). What is the goal of sensory coding? Neural Computation 6, 559–601.CrossRefGoogle Scholar
Földiàk, P. (1990). Forming sparse representations by local anti-Hebbian learning. Biological Cybernetics 64, 165–170.CrossRefGoogle ScholarPubMed
Hamker, F.H. (2004). A dynamic model of how feature cues guide spatial attention. Vision Research 44, 501–521.CrossRefGoogle ScholarPubMed
Hamker, F.H. (2005). The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision. Computer Vision and Image Understanding 100(1–2), 64–106.CrossRefGoogle Scholar
Hamker, F.H. & Wiltschut, J. (2007). Hebbian learning in a model with dynamic rate coded neurons: An alternative to the generative model approach for learning receptive fields from natural scenes. Network: Computation in Neural Systems 18, 249–266.CrossRefGoogle Scholar
Hateren, J.H.V. (1993). Spatiotemporal contrast sensitivity of early vision. Vision Research 33, 257–267.CrossRefGoogle ScholarPubMed
Hoyer, P.O. (2004). Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469.Google Scholar
Hoyer, P.O. & Hyvärinen, A. (2000). Independent component analysis applied to feature extraction from colour and stereo images. Network 11(3), 191–210.CrossRefGoogle ScholarPubMed
Hubel, D.H. & Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology 160, 106–154.CrossRefGoogle ScholarPubMed
Hyvärinen, A., Karhunen, J. & Oja, E. (2001). Independent Component Analysis. New York: Wiley.CrossRefGoogle ScholarPubMed
Karklin, Y. & Lewicki, M.S. (2003). Learning higher-order structures in natural images. Network 14(3), 483–499.CrossRefGoogle ScholarPubMed
Kraskov, A., Stgbauer, H. & Grassberger, P. (2004). Estimating mutual information. Phys. Rev. E, 69(6 Pt. 2), 066138.Google ScholarPubMed
Laughlin, S.B. (1981). Simple coding procedure enhances a neuron’s information capacity. Zeitsch Naturforschung 36C, 910–912.Google Scholar
Laughlin, S.B., de Ruyter van Steveninck, R.R. & Anderson, J.C. (1998). The metabolic cost of neural information. Nature Neuroscience 1, 36–41.CrossRefGoogle ScholarPubMed
Levy, W.B. & Baxter, R.A. (1996). Energy efficient neural codes. Neural Computation 8, 531–543.CrossRefGoogle ScholarPubMed
Lewicki, M.S., Hughes, H. & Olshausen, B.A. (1999). Probabilistic framework for the adaptation and comparison of image codes. Journal of Optical Society America A 16, 1587–1601.CrossRefGoogle Scholar
Marr, D. & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London. Series B, Biological Sciences 207(1167), 187–217.Google ScholarPubMed
Oja, E. (1982). A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology 15, 267–273.CrossRefGoogle ScholarPubMed
Olshausen, B.A. & Field, D.J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609.CrossRefGoogle ScholarPubMed
Olshausen, B.A. & Field, D.J. (1997). Sparse coding with an over complete basis set: A strategy employed by V1? Vision Research 37, 3311–3325.CrossRefGoogle Scholar
Rehn, M. & Sommer, F.T. (2007). A network that uses few active neurons to code visual input predicts the diverse shapes of cortical receptive fields. Journal of Computational Neuroscience 22(2), 135–146.CrossRefGoogle ScholarPubMed
Ren, M., Yoshimura, Y., Takada, N., Horibe, S. & Komatsu, Y. (2007). Specialized inhibitory synaptic actions between nearby neocortical pyramidal neurons. Science 316(5825), 758–761.CrossRefGoogle ScholarPubMed
Ringach, D.L., Bredfeldt, C.E., Shapley, R.M. & Hawken, M.J. (2002). Suppression of neural responses to nonoptimal stimuli correlates with tuning selectivity in macaque V1. Journal of Neurophysiology 87, 1018–1027.CrossRefGoogle ScholarPubMed
Schwartz, O. & Simoncelli, E.P. (2001). Natural signal statistics and sensory gain control. Nature Neuroscience 4, 819–825.CrossRefGoogle ScholarPubMed
Sejnowski, T.J. (1977). Storing covariance with nonlinearly interacting neurons. Journal of Mathematical Biology 4, 303–321.CrossRefGoogle ScholarPubMed
Simoncelli, E.P. & Olshausen, B.A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience 24, 1193–1216.CrossRefGoogle ScholarPubMed
Valois, R.L.D., Yund, E.W. & Hepler, N. (1982). The orientation and direction selectivity of cells in macaque visual cortex. Vision Research 22, 531–544.CrossRefGoogle ScholarPubMed
van Hateren, J.H. & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B, Biological Sciences 265(1394), 359–366.CrossRefGoogle ScholarPubMed
Vinje, W.E. & Gallant, J.L. (2000). Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287(5456), 1273–1276.CrossRefGoogle ScholarPubMed
Watters, P.A. (2004). Coding distributed representations of natural scenes: A comparison of orthogonal and non-orthogonal models. Neurocomputing 61, 277–289.CrossRefGoogle Scholar
Weber, C. & Triesch, J. (2008). A sparse generative model of v1 simple cells with intrinsic plasticity. Neural Computation 20, 1261–1284.CrossRefGoogle ScholarPubMed
Willmore, B., Watters, P.A. & Tolhurst, D.J. (2000). A comparison of natural-image-based models of simple-cell coding. Perception 29, 1017–1040.CrossRefGoogle ScholarPubMed
Willshaw, D. & Dayan, P. (1990). Optimal plasticity from matrix memories: What goes up must come down. Neural Computation 2, 85–93.CrossRefGoogle Scholar