Skip to main content
Log in

Independent Component Analysis of Temporal Sequences Subject to Constraints by Lateral Geniculate Nucleus Inputs Yields All the Three Major Cell Types of the Primary Visual Cortex

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

Information maximization has long been suggested as the underlying coding strategy of the primary visual cortex (V1). Grouping image sequences into blocks has been shown by others to improve agreement between experiments and theory. We have studied the effect of temporal convolution on the formation of spatiotemporal filters—that is, the analogues of receptive fields—since this temporal feature is characteristic to the response function of lagged and nonlagged cells of the lateral geniculate nucleus. Concatenated input sequences were used to learn the linear transformation that maximizes the information transfer. Learning was accomplished by means of principal component analysis and independent component analysis. Properties of the emerging spatiotemporal filters closely resemble the three major types of V1 cells: simple cells with separable receptive field, simple cells with nonseparable receptive field, and complex cells.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Attneave F (1954) Some informational aspects of visual perception. Psychol. Rev. 61:183-193.

    Google Scholar 

  • Barlow H (1972) Single units and sensation: A neuron doctrine for perceptual psychology? Perception 1:295-311.

    Google Scholar 

  • Bell A, Sejnowski T (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7:1129-1159.

    Google Scholar 

  • Bell A, Sejnowski T (1997) The “independent components” of natural scenes are edge filters. Vision Res. 37:3327-3338.

    Google Scholar 

  • Buchsbaum G, Gottschalk A (1983) Trichromacy, opponent colours coding and optimum colour information transmission in the retina. Proc. R. Soc. Lond. B 220:89-113.

    Google Scholar 

  • Cai D, DeAngelis G, Freeman R (1997) Spatiotemporal receptive field organization in the lateral geniculate nucleus of cats and kittens. J. Neurophysiol. 78:1045-1061.

    Google Scholar 

  • Calvin W (1999) The MIT Encyclopedia of the Cognitive Sciences. MIT Press, Cambridge, MA. pp. 148-150.

    Google Scholar 

  • Cover T, Thomas J (1991) Elements of Information Theory. Wiley, New York.

    Google Scholar 

  • DeAngelis G, Ohzawa I, Freeman R (1993) Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. J. Neurophysiol. 69:1091-1117.

    Google Scholar 

  • DeAngelis G, Ohzawa I, Freeman R (1999) Functional microorganization of primary visual cortex: Receptive-field analysis of nearby neurons. J. Neuroscience 19:1091-1117.

    Google Scholar 

  • Dong D, Atick J (1995) Temporal decorrelation: A theory of lagged and nonlagged responses in the lateral geniculate nucleus. Network 6:159-178.

    Google Scholar 

  • Field D (1987) Relations between the statistics of natural images and the response properties of cortical cells. J. Optical Soc. America A4:2379-2394.

    Google Scholar 

  • Gordon B, Presson J (1982) Orientation deprivation in cat: What produces the abnormal cells? Exp. Brain. Res. 46(1):144-146.

    Google Scholar 

  • Gordon B, Presson J, Packwood J, Scheer R (1979) Alteration of cortical orientation selectivity: Importance of asymmetric input. Science 204:1109-1111.

    Google Scholar 

  • Graham R, Knuth D, Patashnik O (1989) Concrete Mathematics: A Foundation for Computer Science. Addison-Wesley, Reading, MA.

    Google Scholar 

  • Hammond P (1991) On the response of simple and complex cells to random dot patterns: A reply to Skotton, Grosof and de Valois. Vision Res. 31:47-50.

    Google Scholar 

  • Hateren J, Ruderman D (1998) Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proc. R. Soc. Lond. B 265:2315-2320.

    Google Scholar 

  • Haykin S (1994) Neural Networks: A Comprehensive Foundation. Macmillan, New York. pp. 363-370.

    Google Scholar 

  • Henry G, Mustari M, Bullier J (1983) Different geniculate inputs to B and C cells of cat striate cortex. Exp. Brain Res. 52:179-189.

    Google Scholar 

  • Hotteling H (1933) Analysis of a complex of statistical variables into principal components. J. Educational Psychol. 24:417-441, 498-520.

    Google Scholar 

  • Hubel D, Wiesel T (1959) Receptive fields of single neurones in the cat's striate cortex. J. Physiol. 148:574-591.

    Google Scholar 

  • Hyvärinen A (1999a) Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Neural Comput. 11:1739-1768.

    Google Scholar 

  • Hyvärinen A (1999b) Survey on independent component analysis. Neural Computing Surveys 2:94-128.

    Google Scholar 

  • Hyvärinen A, Hoyer P, Oja E (1999) Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation. In: Advances in Neural Information Processing Systems 11 (NIPS*98). MIT Press, Cambridge, MA. pp. 1739-1768.

    Google Scholar 

  • Hyvärinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput. 9:1483-1492.

    Google Scholar 

  • Lisman J (1999) Relating hippocampal circuitry to function: Recall of memory sequences by reciprocal dentate-CA3 interactions. Neuron 22:233-242.

    Google Scholar 

  • Lisman J, Idiart M (1995) A mechanism for storing 7 ± 2 short-term memories in oscillatory subcycles. Science 267:1512-1514.

    Google Scholar 

  • Lórincz A, Szatmáry B, Kabán A (2000) Independent component analysis of temporally convolved natural image sequences yields spatio-temporal filters similar to cells in primary visual cortex. In: J Bower, ed. Trends in Research: Computational Neuroscience, 2000. Plenum Press, New York.

    Google Scholar 

  • Mignard M, Malpeli J (1991) Paths of information flow through visual cortex. Science 251:1249-1251.

    Google Scholar 

  • Olshausen B (1996) Learning linear, sparse, factorial codes. Technical Report AI Memo 1580, CBCL 138, Artificial Intelligence Lab, MIT, Cambridge, MA.

    Google Scholar 

  • Olshausen B, Field D (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607-609.

    Google Scholar 

  • Olshausen B, Field D (1997) Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Res. 37:3311-3325.

    Google Scholar 

  • Parker A, Hawken M (1987) Hyperacuity and the visual cortex. Nature 326:105-106.

    Google Scholar 

  • Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2:559-572.

    Google Scholar 

  • Saul A, Humphrey A (1990) Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. J. Neurophysiol. 64:206-224.

    Google Scholar 

  • Sillito A (1979) Inhibitory mechanisms influencing complex cell orientation selectivity and their modification at high resting discharge levels. J. Physiol. (Lond.) 289:33-53.

    Google Scholar 

  • Tailor D, Finkel L, Buchsbaum G (2000) Color opponent receptive fields derived from independent component analysis of natural images. Vision Res. 40:2671-2676.

    Google Scholar 

  • van Hateren J, van der Schaaf A (1998) Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond. B 265:1-8.

    Google Scholar 

  • Westheimer G (1981) Visual hyperacuity. Prog. Sensory Physiol. 1:1-37.

    Google Scholar 

  • Wimbauer S, Wenish O, Miller K, van Hemmen J (1997) Development of spatiotemporal receptive fields of simple cells: I. Model formulation. Biological Cybernetics 77:456-461.

    Google Scholar 

  • Zigmund M, Bloom F, Landis S, Roberts J, Squire L (1999) Fundamentals of Neuroscinece. San Diego, Academic Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Szatmáry, B., Lőrincz, A. Independent Component Analysis of Temporal Sequences Subject to Constraints by Lateral Geniculate Nucleus Inputs Yields All the Three Major Cell Types of the Primary Visual Cortex. J Comput Neurosci 11, 241–248 (2001). https://doi.org/10.1023/A:1013723131070

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1013723131070

Navigation