Spatial and temporal jitter distort estimated functional properties of visual sensory neurons

Journal of Computational Neuroscience volume 27, pages309–319(2009)Cite this article

Abstract

The functional properties of neural sensory cells or small neural ensembles are often characterized by analyzing response-conditioned stimulus ensembles. Many widely used analytical methods, like receptive fields (RF), Wiener kernels or spatio-temporal receptive fields (STRF), rely on simple statistics of those ensembles. They also tend to rely on simple noise models for the residuals of the conditional ensembles. However, in many cases the response-conditioned stimulus set has more complex structure. If not taken explicitly into account, it can bias the estimates of many simple statistics, and lead to erroneous conclusions about the functionality of a neural sensory system. In this article, we consider sensory noise in the visual system generated by small stimulus shifts in two dimensions (2 spatial or 1-space 1-time jitter). We model this noise as the action of a set of translations onto the stimulus that leave the response invariant. The analysis demonstrates that the spike-triggered average is a biased estimator of the model mean, and provides a de-biasing method. We apply this approach to observations from the stimulus/response characteristics of cells in the cat visual cortex and provide improved estimates of the structure of visual receptive fields. In several cases the new estimates differ substantially from the classic receptive fields, to a degree that may require re-evaluation of the functional description of the associated cells.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Aldworth, Z. N., Miller, J. P., Gedeon, T., Cummins, G. I., & Dimitrov, A. G. (2005). Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature sensitivity. Journal of Neuroscience, 25(22), 5323–5332.

    Article  CAS  PubMed  Google Scholar 

  2. Bryant, H. L., & Segundo, J. P. (1976). Spike initiation by transmembrane current: A white-noise analysis. Journal of Physiology, 260, 279–314.

    CAS  PubMed  Google Scholar 

  3. Chang, T.-R., Chung, P.-C., Chiu, T.-W., & Poon, P. W.-F. (2005). A new method for adjusting neural response jitter in the STRF obtained by spike-trigger averaging. BioSystems, 79, 213–222.

    Article  PubMed  Google Scholar 

  4. 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 Neuroscience, 69(14), 1091–1117.

    CAS  Google Scholar 

  5. Dimitrov, A. G., & Gedeon, T. (2006). Effects of stimulus transformations on the perceived function of sensory neurons. JCNS, 20, 265–283.

    Google Scholar 

  6. Eggermont, J. J., Sersten, A. M., & Johannesma, P. I. (1983). Prediction of the responses of auditory neurons in the midbrain of grass frog based on the spectro-temporal receptive field. Hearing Research, 10, 191–202.

    Article  CAS  PubMed  Google Scholar 

  7. Forte, J., Peirce, J., Kraft, J. M., Krauskopf, J., & Lennie, P. (2002). Residual eye-movements in macaque and their effects on visual responses of neurons. Visual Neuroscience, 19(1), 31–38.

    Article  PubMed  Google Scholar 

  8. Frey, B. J., & Jojic, N. (2003). Transformation-invariant clustering using the EM algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1), 1–17.

    Article  Google Scholar 

  9. Gawne, T. J., Kjaer, T. W., Hertz, J. A., & Richmond, B. J. (1996). Adjacent visual cortical complex cells share about 20% of their stimulus-related information. Cerebral Cortex, 6(3), 482–489.

    Article  CAS  PubMed  Google Scholar 

  10. Gollisch, T. (2006). Estimating receptive fields in the presence of spike-time jitter. Network: Computation in Neural Systems, 17, 103–129.

    Article  Google Scholar 

  11. Gur, M., Kagan, I., & Snodderly, D. M. (2005). Orientation and direction selectivity of neurons in V1 of alert monkeys: Functional relationships and laminar distributions. Cerebral Cortex, 15(8), 1207–1221.

    Article  PubMed  Google Scholar 

  12. Jones, J. P., & Palmer, L. A. (1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58, 1233–1258.

    CAS  PubMed  Google Scholar 

  13. Kennedy, H., Martin, K. A., Orban, G. A., & Whitteridge, D. (1985). Receptive field properties of neurones in visual area 1 and visual area 2 in the baboon. Neuroscience, 14(2), 405–415.

    Article  CAS  PubMed  Google Scholar 

  14. Kjaer, T. W., Gawne, T. J., Hertz, J. A., & Richmond, B. J. (1997). Insensitivity of V1 complex cell responses to small shifts in the retinal image of complex patterns. Journal of Neuroscience, 78(6), 3187–3197.

    CAS  Google Scholar 

  15. Krzanowski, W. J., & Marriott, F. H. C. (1995). Multivariate analysis part 2: classification, covariance structures and repeated measurements. In Kendall’s Library of Statistics 2. London: Edward Arnold.

    Google Scholar 

  16. Levitt, J. B., Kiper, D. C., & Movshon, J. A. (1994). Receptive fields and functional architecture of macaque V2. Journal of Neuroscience, 71(6), 2517–2542.

    CAS  Google Scholar 

  17. Mainen, Z.G., & Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science, 268(5216), 1503–1506.

    Article  CAS  PubMed  Google Scholar 

  18. Maldonado, P. E., & Gray, C. M. (1996). Heterogeneity in local distributions of orientation-selective neurons in the cat primary visual cortex. Visual Neuroscience, 13(3), 509–516.

    Article  CAS  PubMed  Google Scholar 

  19. Malone, B. J., Kumar, V. R., & Ringach, D. L. (2007). Dynamics of receptive field size in primary visual cortex. Journal of Neuroscience, 97(1), 407–414.

    Google Scholar 

  20. Martinez-Conde, S., Macknik, S. L., & Hubel, D. H. (2002). The function of bursts of spikes during visual fixation in the awake primate lateral geniculate nucleus and primary visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 99(21), 13920–13925.

    Article  CAS  PubMed  Google Scholar 

  21. Mazer, J. A., Vinje, W. E., McDermott, J., Schiller, P. H., & Gallant, J. L. (2002). Spatial frequency and orientation tuning dynamics in area V1. Proceedings of the National Academy of Sciences of the United States of America, 99(3), 1645–1650.

    Article  CAS  PubMed  Google Scholar 

  22. Meister, M., Pine, J., & Baylor, D.A. (1994). Multi-neuronal signals from the retina: Acquisition and analysis. Journal of Neuroscience Methods, 51(1), 95–106.

    Article  CAS  PubMed  Google Scholar 

  23. Michelson, A. (1927). Studies in optics. Chicago: University of Chicago Press.

    Google Scholar 

  24. Olshausen, B. A., Anderson, C. H., & van Essen, D. C. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13(11), 4700–4719.

    CAS  PubMed  Google Scholar 

  25. Olshausen, B. A., & Field, D. J. (2005). What is the other 85% of V1 doing? In J. L. van Hemmen, & T. J. Sejnoski (Eds.), 23 problems in systems neuroscience. Oxford: Oxford University Press.

    Google Scholar 

  26. Poon, P. W.-F., & Yu, P. P. (2000). Spectro-temporal receptive fields of midbrain auditory neurons in the rat obtained with frequency modulated stimulation. Neuroscience Letters, 289, 9–12.

    Article  CAS  PubMed  Google Scholar 

  27. Rao, R., & Ruderman, D. (1999). Learning Lie groups for invariant visual perception. In M. S. Kearns, S. A. Solla, & D. A. Cohn (Eds.), Advances in NIPS, (Vol. 11, pp. 810–816). Cambridge: MIT.

    Google Scholar 

  28. Reich, D. S., Mechler, F., Purpura, K. P., & Victor, J. D. (2000). Interspike intervals, receptive fields, and information encoding in primary visual cortex. Journal of Neuroscience, 20, 1964–1974.

    CAS  PubMed  Google Scholar 

  29. Reid, R. C., & Alonso, J. M. (1995). Specifcity of monosynaptic connections from thalamus to visual cortex. Nature, 378(6554), 281–284.

    Article  CAS  PubMed  Google Scholar 

  30. Reid, R., Victor, J. D., & Shapley, R. M. (1997). The use of m-sequences in the analysis of visual neurons: Linear receptive field properties. Visual Neuroscience, 14(6), 1015–1027.

    Article  CAS  PubMed  Google Scholar 

  31. Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1997) Spikes: Exploring the neural code. Cambridge: MIT.

    Google Scholar 

  32. Ringach, D. L. (2002) Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. Journal of Neuroscience, 88(1), 455–463.

    Google Scholar 

  33. Ringach, D. L., Hawken, M., & Shapley, R. (1997). Dynamics of orientation tuning in macaque primary visual cortex. Nature, 387(6630), 281–284.

    Article  CAS  PubMed  Google Scholar 

  34. Ringach, D. L., Shapley, R. L., & Hawken, M. J. (2002). Orientation selectivity in macaque V1: diversity and laminar dependence. Journal of Neuroscience, 22(13), 5639–5651.

    CAS  PubMed  Google Scholar 

  35. Rust, N. C., Schwartz, O., Movshon, J. A., & Simoncelli, E. (2004). Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1. Neurocomputing, 58–60, 793–799.

    Article  Google Scholar 

  36. Schwartz, O., Pillow, J. W., Rust, N. C., & Simoncelli, E. P. (2006). Spike-triggered neural characterization. Journal of Visualization, 6, 484–507.

    Article  Google Scholar 

  37. Simoncelli, E. P., Paninski, L., Pillow, J., & Schwartz, O. (2004). Characterization of neural responses with stochastic stimuli. In M. Gazzaniga (Ed.), The new cognitive neurosciences (3rd edn.). Cambridge: MIT.

    Google Scholar 

  38. Theunissen, F. E., Woolley, S. M., Hsu, A., & Fremouw, T. (2004). Methods for the analysis of auditory processing in the brain. Annals of the New York Academy of Sciences, 1016, 187–207.

    Article  PubMed  Google Scholar 

  39. Whittle, P. (1994). The psychophysics of contrast brightness. In A. L. Gilchrist (Ed.), Lightness, brightness, and transparency (pp. 35–110). Hillsdale: Lawrence Erlbaum.

    Google Scholar 

  40. Yen, S., Baker, J., & Gray, C. M. (2007). Heterogeneity in the responses of adjacent neurons to natural stimuli in cat striate cortex. Journal of Neurophysiology, 97(2), 1326–1341.

    Article  PubMed  Google Scholar 

Download references

Author information

Affiliations

  1. Center for Computational Biology, Montana State University, Bozeman, MT, 59717, USA

    Alexander G. Dimitrov, Melissa A. Sheiko & Jonathan Baker

  2. National University of Singapore, Blk E4, #05-45, 4 Engineering Drive 3, Singapore, 117576, Republic of Singapore

    Shih-Cheng Yen

Authors
  1. Alexander G. Dimitrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melissa A. Sheiko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shih-Cheng Yen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander G. Dimitrov.

Additional information

Action Editor: Jonathan David Victor

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Dimitrov, A.G., Sheiko, M.A., Baker, J. et al. Spatial and temporal jitter distort estimated functional properties of visual sensory neurons. J Comput Neurosci 27, 309–319 (2009). https://doi.org/10.1007/s10827-009-0144-8

Download citation

Keywords

  • Dejittering
  • Spatial
  • Spatio-temporal
  • Jitter
  • Cat
  • Visual cortex
  • V1