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Analyzing receptive fields, classification images and functional images: challenges with opportunities for synergy

Nature Neuroscience volume 8, pages 1651–1656 (2005)Cite this article

Abstract

In neurophysiology, psychophysics, optical imaging and functional imaging studies, the investigator seeks a relationship between a high-dimensional variable, such as an image, and a categorical variable, such as the presence or absence of a spike or a behavior. The usual analysis strategy is fundamentally identical across these contexts—it amounts to calculating the average value of the high-dimensional variable for each value of the categorical variable and comparing these results by subtraction. Though intuitive and straightforward, this procedure may be inaccurate or inefficient and may overlook important details. Sophisticated approaches have been developed within these several experimental contexts, but they are rarely applied beyond the context in which they were developed. Recognition of the relationships among these contexts has the potential to accelerate improvements in analytic methods and to increase the amount of information that can be gleaned from experiments.

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Figure 1: Two kinds of experiments in which a highly multivariate quantity (yellow box) is to be related to a categorical quantity (purple box).
Figure 2: A geometric view of associations of multivariate and categorical data.

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References

  1. Sutter, E. in Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks (eds. Pinter, R. & Nabet, B.) 171–220 (CRC Press, Cleveland, 1992).

    Google Scholar 

  2. Chichilnisky, E.J. A simple white noise analysis of neuronal light responses. Network 12, 199–213 (2001).

    Article  CAS  Google Scholar 

  3. Marmarelis, P.Z. & Naka, K. White-noise analysis of a neuron chain: an application of the Wiener theory. Science 175, 1276–1278 (1972).

    Article  CAS  Google Scholar 

  4. Simoncelli, E., Paninski, L., Pillow, J. & Schwartz, O. in The Cognitive Neurosciences 3rd edn. (ed. Gazzaniga, M.) (MIT Press, Cambridge, Massachusetts, 2004).

    Google Scholar 

  5. Touryan, J., Lau, B. & Dan, Y. Isolation of relevant visual features from random stimuli for cortical complex cells. J. Neurosci. 22, 10811–10818 (2002).

    Article  CAS  Google Scholar 

  6. Sharpee, T., Rust, N.C. & Bialek, W. Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput. 16, 223–250 (2004).

    Article  Google Scholar 

  7. David, S.V., Vinje, W.E. & Gallant, J.L. Natural stimulus statistics alter the receptive field structure of V1 neurons. J. Neurosci. 24, 6991–7006 (2004).

    Article  CAS  Google Scholar 

  8. Rust, N.C., Schwartz, O., Movshon, J.A. & Simoncelli, E.P. Spatiotemporal elements of macaque V1 receptive fields. Neuron 46, 945–956 (2005).

    Article  CAS  Google Scholar 

  9. Escabi, M.A. & Schreiner, C.E. Nonlinear spectrotemporal sound analysis by neurons in the auditory midbrain. J. Neurosci. 22, 4114–4131 (2002).

    Article  CAS  Google Scholar 

  10. Machens, C.K., Wehr, M.S. & Zador, A.M. Linearity of cortical receptive fields measured with natural sounds. J. Neurosci. 24, 1089–1100 (2004).

    Article  CAS  Google Scholar 

  11. Eckstein, M.P. & Ahumada, A.J., Jr. Classification images: a tool to analyze visual strategies. J. Vis. 2, 1x (2002).

    Article  Google Scholar 

  12. Ahumada, A.J., Jr & Lovell, J. Stimulus features in signal detection. J. Acoust. Soc. Am. 49, 1751–1756 (1971).

    Article  Google Scholar 

  13. Grinvald, A. Optical imaging of architecture and function in the living brain sheds new light on cortical mechanisms underlying visual perception. Brain Topogr. 5, 71–75 (1992).

    Article  CAS  Google Scholar 

  14. Kwong, K.K. et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. USA 89, 5675–5679 (1992).

    Article  CAS  Google Scholar 

  15. Ohzawa, I., DeAngelis, G.C. & Freeman, R.D. Encoding of binocular disparity by complex cells in the cat's visual cortex. J. Neurophysiol. 77, 2879–2909 (1997).

    Article  CAS  Google Scholar 

  16. Pesaran, B., Pezaris, J.S., Sahani, M., Mitra, P.P. & Andersen, R.A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805–811 (2002).

    Article  CAS  Google Scholar 

  17. Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A. & Arieli, A. Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003).

    Article  CAS  Google Scholar 

  18. Rodriguez, E. et al. Perception's shadow: long-distance synchronization of human brain activity. Nature 397, 430–433 (1999).

    Article  CAS  Google Scholar 

  19. Lee, Y. & Schetzen, M. Measurement of the kernels of a nonlinear system by cross-correlation. Int. J. Control 2, 237–254 (1965).

    Article  Google Scholar 

  20. de Ruyter van Steveninck, R. & Bialek, W. Real-time performance of a movement sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences. Proc. R. Soc. Lond. B. 234, 379–414 (1988).

    Article  Google Scholar 

  21. Victor, J.D., Shapley, R.M. & Knight, B.W. Nonlinear analysis of cat retinal ganglion cells in the frequency domain. Proc. Natl. Acad. Sci. USA 74, 3068–3072 (1977).

    Article  CAS  Google Scholar 

  22. Smyth, D., Willmore, B., Baker, G.E., Thompson, I.D. & Tolhurst, D.J. The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation. J. Neurosci. 23, 4746–4759 (2003).

    Article  CAS  Google Scholar 

  23. Theunissen, F.E. et al. Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network 12, 289–316 (2001).

    Article  CAS  Google Scholar 

  24. Korenberg, M.J., Bruder, S.B. & McIlroy, P.J. Exact orthogonal kernel estimation from finite data records: extending Wiener's identification of nonlinear systems. Ann. Biomed. Eng. 16, 201–214 (1988).

    Article  CAS  Google Scholar 

  25. Levi, D.M. & Klein, S.A. Classification images for detection and position discrimination in the fovea and parafovea. J. Vis. 2, 46–65 (2002).

    Article  Google Scholar 

  26. Simoncelli, E.P. & Olshausen, B.A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

    Article  CAS  Google Scholar 

  27. Fisher, R.A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936).

    Article  Google Scholar 

  28. Gabbay, M., Brennan, C., Kaplan, E. & Sirovich, L. A principal components-based method for the detection of neuronal activity maps: application to optical imaging. Neuroimage 11, 313–325 (2000).

    Article  CAS  Google Scholar 

  29. Yokoo, T., Knight, B.W. & Sirovich, L. An optimization approach to signal extraction from noisy multivariate data. Neuroimage 14, 1309–1326 (2001).

    Article  CAS  Google Scholar 

  30. Everson, R., Knight, B.W. & Sirovich, L. Separating spatially distributed response to stimulation from background. I. Optical imaging. Biol. Cybern. 77, 407–417 (1997).

    Article  CAS  Google Scholar 

  31. Friston, K.J., Frith, C.D., Frackowiak, R.S. & Turner, R. Characterizing dynamic brain responses with fMRI: a multivariate approach. Neuroimage 2, 166–172 (1995).

    Article  CAS  Google Scholar 

  32. Worsley, K.J., Poline, J.B., Friston, K.J. & Evans, A.C. Characterizing the response of PET and fMRI data using multivariate linear models. Neuroimage 6, 305–319 (1997).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  34. Thomas, C.G., Harshman, R.A. & Menon, R.S. Noise reduction in BOLD-based fMRI using component analysis. Neuroimage 17, 1521–1537 (2002).

    Article  Google Scholar 

  35. Carmona, R.A., Hwang, W.L. & Frostig, R.D. Wavelet analysis for brain function imaging. IEEE Trans. Med. Imaging 14, 556–564 (1995).

    Article  CAS  Google Scholar 

  36. Tikhonov, A.N. & Arsenin, V.Y. Solutions of Ill-Posed Problems (Wiley, New York, 1977).

    Google Scholar 

  37. Hastie, T., Buja, A. & Tibshirani, R. Penalized discriminant analysis. Ann. Stat. 23, 73–102 (1995).

    Article  Google Scholar 

  38. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer-Verlag, New York, 2001).

    Book  Google Scholar 

  39. Boynton, G.M., Engel, S.A., Glover, G.H. & Heeger, D.J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16, 4207–4221 (1996).

    Article  CAS  Google Scholar 

  40. Paninski, L. Convergence properties of three spike-triggered analysis techniques. Network 14, 437–464 (2003).

    Article  Google Scholar 

  41. Aguera y Arcas, B. & Fairhall, A.L. What causes a neuron to spike? Neural Comput. 15, 1789–1807 (2003).

    Article  Google Scholar 

  42. Pillow, J. & Simoncelli, E. Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing 52–54, 109–115 (2003).

    Article  Google Scholar 

  43. Neri, P. Estimation of nonlinear psychophysical kernels. J. Vis. 4, 82–91 (2004).

    Article  Google Scholar 

  44. Neri, P. & Heeger, D.J. Spatiotemporal mechanisms for detecting and identifying image features in human vision. Nat. Neurosci. 5, 812–816 (2002).

    Article  CAS  Google Scholar 

  45. Field, D.J. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4, 2379–2394 (1987).

    Article  CAS  Google Scholar 

  46. Dong, D.W. & Atick, J.J. Statistics of natural time-varying images. Netw. Comput. Neural Syst. 6, 345–358 (1995).

    Article  Google Scholar 

Download references

Acknowledgements

The author thanks S. Klein, T. Yokoo, L. Paninksi and P. Buzás for helpful discussions.This work is supported in part by grants EY7977 and EY9314 to J.D.V.

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  1. the Department of Neurology & Neuroscience, Weill Medical College of Cornell University, 1300 York Avenue, New York, 10021, New York, USA

    Jonathan D Victor

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Victor, J. Analyzing receptive fields, classification images and functional images: challenges with opportunities for synergy. Nat Neurosci 8, 1651–1656 (2005). https://doi.org/10.1038/nn1607

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