Advertisement

Cortical Belief Networks

  • Richard S. Zemel

Abstract

Most theoretical and empirical studies of cortical population codes make the assumption that underlying neuronal activities is a unique and unambiguous value of an encoded quantity. We propose an alternative hypothesis, that neural populations represent, and effectively compute, probabilities. Under this hypothesis, population activities can contain additional information about such things as multiple values of, or uncertainty about, the quantity. We discuss methods for recovering this extra information, and show how this approach bears on psychophysical and neurophysiological studies. A natural extension of this probabilistic interpretation hypothesis casts interacting populations as a belief network, a structure which permits the analysis of information propagation from one population to another. This novel framework for population codes opens up new avenues for studying a diverse set of problems, including cue combination, decision-making, and visual attention.

Keywords

Receptive Field Medial Temporal Motion Stimulus Population Response Neural Population 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, C.H. (1994) Basic elements of biological computational systems. International Journal of Modern Physics C 5(2): 135-137.CrossRefGoogle Scholar
  2. Anderson, C.H. (1995) Unifying perspectives on neuronal codes and processing. In: XIX International workshop on condensed matter theories. Caracas, Venezuela.Google Scholar
  3. Anderson, C.H., Van Essen, D.C. (1995) Neurobiological computational systems. In: IEEE World Congress on Computational Intelligence, pp. 213-222.Google Scholar
  4. Basso, M.A., Wurtz, R.H. (1998) Modulation of neuronal activity in superior colliculus by changes in target probability. Journal of Neuroscience 18(18): 7519-7534.Google Scholar
  5. Bastian, A., Riehle, A., Erlhagen, W., Schoner, G. (1998) Prior information preshapes the population representation of movement direction in motor cortex. Neuroreport 9(2): 315-319.CrossRefGoogle Scholar
  6. Britten, K.H., Shadlen, M.N., Newsome, W.T., Movshon, J.A. (1992) The analysis of visual motion: A comparison of neuronal and psychophysical performance. Journal of Neuroscience 12(12): 4745-4765.Google Scholar
  7. Carandini, M., Ringach, D. (1997) Predictions of a recurrent model of orientation selectivity. Vision Res 37: 3061-3071.CrossRefGoogle Scholar
  8. DeAngelis, G.C., Robson, J.G., Ohzawa, I., Freeman, R.D. (1992) Organization of suppression in receptive fields of neurons in cat visual cortex. J. Neurophysiol. 68: 144-163.Google Scholar
  9. Grunewald, A. (1996) A model of transparent motion and non-transparent motion aftereffects. In: D.S. Touretzky, M.C. Mozer, M.E. Hasselmo (Eds.) Advances in Neural Information Processing Systems 8. Cambridge, MA: MIT Press, pp. 837-843.Google Scholar
  10. Hol, K., Treue, S. (1997) Direction-selective responses in the superior temporal sulcus to transparent patterns moving at acute angles. Society for Neuroscience Abstracts 23: 459.Google Scholar
  11. Mather, G., Moulden, B. (1983) Thresholds for movement direction: two directions are less detectable than one. Quarterly Journal of Experimental Psychology 35: 513-518.CrossRefGoogle Scholar
  12. Pouget, A., Sejnowski, T.J. (1997) Spatial transformations in the parietal cortex using basis functions. Journal of Cognitive Neuroscience 9(2): 222-237.CrossRefGoogle Scholar
  13. Rauber, H.J., Treue, S. (1997) Recovering the directions of visual motion in transparent patterns. Society for Neuroscience Abstracts 23: 179.Google Scholar
  14. Recanzone, G.H., Wurtz, R.H., Schwarz, U. (1997) Responses of MT and MST neurons to one and two moving objects in the receptive field. Journal of Neurophysiology 78(6): 2904-2915.Google Scholar
  15. Rieke, F., Warland, D., de Ruyter van Steveninck, R.R., Bialek, W. (1997) Spikes: Exploring the Neural Code. Cambridge, MA: MIT Press.Google Scholar
  16. Shadlen, M.N., Britten, K.H., Newsome, W.T., Movshon, J.A. (1996) A computational analysis of the relationship between neuronal and behavioral responses to visual motion. Journal of Neuroscience 16(4): 1486-1510.Google Scholar
  17. Simoncelli, E., Adelson, E., Heeger, D. (1991) Probability distributions of optical flow. In: Proceedings 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 310-315.Google Scholar
  18. Simoncelli, E., Heeger, D. (1994) A velocity representation model for MT cells. Investigative Opthamology and Visual Science Supplement 35: 1827.Google Scholar
  19. Simoncelli, E., Heeger, D. (1998) A model of neuronal responses in visual area MT. Vision Research 38(5): 743-761.CrossRefGoogle Scholar
  20. Snippe, H.P. (1996) Theoretical considerations for the analysis of population coding in motor cortex. Neural Computation 8(3): 29-37.CrossRefGoogle Scholar
  21. Sompolinsky, H., Shapley, R. (1997) New perspectives on the mechanisms for orientation selectivity. Curr. Opin. Neurobiol. 7: 514-522.CrossRefGoogle Scholar
  22. Treue, S., Hol, K., Rauber, H.-J. (2000) Seeing multiple directions of motion: Physiology and psychophysics. Nature Neuroscience 3(3): 271-277.CrossRefGoogle Scholar
  23. van Wezel, R.J., Lankheet, M.J., Verstraten, F.A., Maree, A.F., van de Grind, W.A. (1996) Responses of complex cells in area 17 of the cat to bi-vectorial transparent motion. Vision Research 36(18): 2805- 2813.CrossRefGoogle Scholar
  24. Weiss, Y., Adelson, T. (1998) Slow and smooth: Combination of local motion signals. Vision Research 31(2): 275-286.Google Scholar
  25. Williams, D., Sekuler, R. (1984) Coherent global motion percepts from stochastic local motions. Vision Research 24(1): 55-62.CrossRefGoogle Scholar
  26. Williams, D., Tweten, S., Sekuler, R. (1991) Using metamers to explore motion perception. Vision Research 31(2): 275-286.CrossRefGoogle Scholar
  27. Zemel, R.S., Dayan, P., Pouget, A. (1998) Probabilistic interpretation of population codes. Neural Computation 10: 403- 430.CrossRefGoogle Scholar
  28. Zemel, R.S., Dayan, P. (1999) Distributional population codes and multiple motion models. In: M.S. Kearns, S.A. Solla, D.A. Cohn (Eds.) Advances in Neural Information Processing Systems 11. Cambridge, MA: MIT Press.Google Scholar
  29. Zemel, R.S., Pillow, J. (1999) Encoding multiple orientations in a recurrent network. Proceedings of the Computational Neuroscience Society, 8, Kluwer Press.Google Scholar
  30. Zhang, K., Ginzburg, I., McNaughton, B.L., Sejnowski, T.J. (1998) Interpreting neuronal population activity by reconstruction: A unified framework with application to hippocampal place cells. Journal of Neurophysiology 79(2): 1017-1044.Google Scholar

Copyright information

© Springer-Verlag London Limited 2003

Authors and Affiliations

  • Richard S. Zemel

There are no affiliations available

Personalised recommendations