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Cognitive Neurodynamics

, Volume 1, Issue 3, pp 261–272 | Cite as

Robustness of neural codes and its implication on natural image processing

  • Sheng LiEmail author
  • Si Wu
Research Article

Abstract

In this study, based on the view of statistical inference, we investigate the robustness of neural codes, i.e., the sensitivity of neural responses to noise, and its implication on the construction of neural coding. We first identify the key factors that influence the sensitivity of neural responses, and find that the overlap between neural receptive fields plays a critical role. We then construct a robust coding scheme, which enforces the neural responses not only to encode external inputs well, but also to have small variability. Based on this scheme, we find that the optimal basis functions for encoding natural images resemble the receptive fields of simple cells in the striate cortex. We also apply this scheme to identify the important features in the representation of face images and Chinese characters.

Keywords

Robust coding Neural codes Natural image processing Neuronal variability V1 

Notes

Acknowledgements

We are very grateful to Peter Dayan. Without his instructive and inspirational discussions, the paper would exist in a rather different form. We also acknowledge valuable comments from Kingsley Sage and Jim Stone.

References

  1. Atick JJ (1992) Could information theory provide an ecological theory of sensory processing? Network-Comp Neural 3:213–251CrossRefGoogle Scholar
  2. Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61:183–193PubMedCrossRefGoogle Scholar
  3. Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith WA (ed) Sensory communication. MIT Press, Cambridge, MAGoogle Scholar
  4. Barlow HB (1989) Unsupervised learning. Neural Comput 1:295–311Google Scholar
  5. Becker S (1993) Learning to categorize objects using temporal coherence. In: Hanson SJ, Cowan JD, Giles CL (eds) Advances in neural information processing systems 5. Morgan Kaufmann, San Mateo, CAGoogle Scholar
  6. Bell AJ, Sejnowski TJ (1997) The independent components of natural scenes are edge filters. Vision Res 37:3327–3338PubMedCrossRefGoogle Scholar
  7. Bishop CM (1996) Neural networks for pattern recognition. Oxford University PressGoogle Scholar
  8. Field DJ (1994) What is the goal of sensory coding? Neural Comput 6:559–601Google Scholar
  9. Földiák P (1991) Learning invariance from transformation sequences. Neural Comput 3:194–200Google Scholar
  10. Hildebrandt TH, Liu WT (1993) Optical recognition of handwritten Chinese characters: Advances since 1980. Pattern Recognit 26:205–225CrossRefGoogle Scholar
  11. Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195:215–243PubMedGoogle Scholar
  12. Hurri J, Hyvärinen A (2003) Simple-cell-like receptive fields maximize temporal coherence in natural video. Neural Comput 15:663–691PubMedCrossRefGoogle Scholar
  13. Hyvärinen A (1999) Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation. Neural Comput 11:1739–1768PubMedCrossRefGoogle Scholar
  14. Laughlin SB (1981) A simple coding procedure enhances a neuron’s information capacity. Z Naturforsch C 36:910–912PubMedGoogle Scholar
  15. Lewicki MS, Olshausen BA (1999) Probabilistic framework for the adaptation and comparison of image codes. J Opt Soc Am A 16:1587–1601Google Scholar
  16. Li S, Wu S (2005) On the variability of cortical neural responses: a statistical interpretation. Neurocomputing 65–66:409–414CrossRefGoogle Scholar
  17. Li Z, Atick JJ (1994) Toward a theory of the striate cortex. Neural Comput 6:127–146Google Scholar
  18. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609PubMedCrossRefGoogle Scholar
  19. Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Res 37:3311–3325PubMedCrossRefGoogle Scholar
  20. Palmer SE (1999) Vision science: photons to phenomenology. MIT Press, Cambridge, MAGoogle Scholar
  21. Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065–1076Google Scholar
  22. Peng D, Ding G, Perry C, Xu D, Jin Z, Luo Q, Zhang L, Deng Y (2004) fMRI evidence for the automatic phonological activation of briefly presented words. Cognitive Brain Res 20:156–164CrossRefGoogle Scholar
  23. Principe JC, Xu D, Fisher JW (2000) Information-theoretic learning. In: Haykin S (ed) Unsupervised adaptive filtering, vol 1: Blind Source Separation. WileyGoogle Scholar
  24. Renyi A (1976) Some fundamental questions of information theory. In: Turan P (ed) Selected papers of Alfred Renyi, vol 2. Akademiai Kiado, BudapestGoogle Scholar
  25. Salinas E (2006) How behavioral constraints may determine optimal sensory representations. PLoS Biol 4(12):e387PubMedCrossRefGoogle Scholar
  26. Schölkopf B, Smola AJ (2001) Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, MAGoogle Scholar
  27. Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24:1193–1216PubMedCrossRefGoogle Scholar
  28. Stone JV (1996) Learning perceptually salient visual parameters using spatiotemporal smoothness constraints. Neural Comput 8:1463–1492PubMedCrossRefGoogle Scholar
  29. van Hateren JH, van der Schaaf A (1998) Independent component filters of natural images compared with simple cells in primary visual cortex. Proc R Soc Lond B 265:359–366CrossRefGoogle Scholar
  30. Vincent BT, Baddeley RJ (2003) Synaptic energy efficiency in retinal processing. Vision Res 43:1283–1290PubMedCrossRefGoogle Scholar
  31. Wiskott L, Sejnowski TJ (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14:715–770PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of SussexFalmer, BrightonUK
  2. 2.School of PsychologyUniversity of BirminghamEdgbaston, BirminghamUK

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