Characterizing responses of translation-invariant neurons to natural stimuli: Maximally informative invariant dimensions

Neural Computation

Volumn 24, Issue 9, 2012, Pages 2384-2421

  Eickenberg, Michael ^a,b   Rowekamp, Ryan J ^a,b   Kouh, Minjoon ^a,b   Sharpee, Tatyana O ^a,b  

^a SALK INSTITUTE FOR BIOLOGICAL STUDIES   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; CYTOLOGY; HUMAN; NERVE CELL; PHOTOSTIMULATION; PHYSIOLOGY; PROBABILITY; SIGNAL NOISE RATIO; VISION; VISUAL CORTEX;

ACTION POTENTIALS; ALGORITHMS; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NEURONS; PHOTIC STIMULATION; PROBABILITY; SIGNAL-TO-NOISE RATIO; VISUAL CORTEX; VISUAL PERCEPTION;

EID: 84871895522     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00330     Document Type: Article

References (106)

1. Adelman, T. L., Bialek, W., & Olberg, R. M. (2003). The information content of receptive fields. Neuron, 40, 823-833.
2. Atencio, C. A., Sharpee, T. O., & Schreiner, C. E. (2008). Cooperative nonlinearities in auditory cortical neurons. Neuron, 58, 956-966.
3. Atencio, C. A., Sharpee, T. O., & Schreiner, C. E. (2009). Hierarchical computation in the canonical auditory cortical circuit. PNAS, 106, 21894-21899.
4. Baddeley, R., Abbott, L. F., Booth, M. C. A., Sengpiel, F., Freeman, T., Wakeman, E. A., et al. (1997). Responses of neurons in primary and inferio temporal visual cortices to natural scenes. Proc. R. Soc. Lond. B, 264, 1775-1783.
5. Bialek, W., & de Ruyter van Steveninck, R. R. (2005). Features and dimensions: Motion estimation in fly vision. q-bio/0505003.
6. Bishop, C. M. (2004). Neural networks for pattern recognition. New York: Oxford University Press.
7. Boussaoud, D., Desimone, R., & Ungerleider, L. (1991). Visual topography of area TEO in the macaque. J. Comp. Neurol., 306, 554-575.
8. Brenner, N., Strong, S. P., Koberle, R., Bialek, W., & de Ruyter van Steveninck, R. R. (2000). Synergy in a neural code. Neural Comput., 12, 1531-1552.
9. Burak, Y., Rokni, U., Meister, M., & Sompolinsky, H. (2010). Bayesian model of dynamic image stabilization in the visual system. PNAS, 107, 19525-19530.
10. Cadieu, C., Kouh, M., Pasupathy, A., Connor, C. E., Riesenhuber, M., & Poggio, T. (2007). A model of V4 shape selectivity and invariance. J. Neurophysiol., 98, 1733-1750.
11. Chen, X., Han, F., Poo, M., & Dan, Y. (2007). Excitatory and suppressive receptive field subunits in awake monkey primary visual cortex (V1). PNAS, 104, 19120-19125.
12. Chichilnisky, E. J. (2001). A simple white noise analysis of neuronal light responses. Network: Comput. Neural Syst., 12, 199-213.
13. Connor, C. E., Brincat, S. L., & Pasupathy, A. (2007). Transformation of shape information in the ventral pathway. Curr. Opin. Neurobiol., 17, 140-147.
14. de Boer, E., & Kuyper, P. (1968). Triggered correlation. IEEE Trans. Biomed. Eng., BME-15, 169-179.
15. de Ruyter van Steveninck, R. R., & Bialek, W. (1988). 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, 265, 259-265.
16. Deangelis, G. C., Ohzawa, I., & Freeman, R. D. (1993). Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. J. Neurophysiol., 69, 1118-1135.
17. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc., 39, 1-38.
18. Desimone, R., Albright, T. D., Gross, C. G., & Bruce, C. (1984). Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci., 4, 2051-2062.
19. Desimone, R., & Schein, S. J. (1987). Visual properties of neurons in area V4 of the macaque: Sensitivity to stimulus form. J. Neurophysiology, 57, 835-868.
20. Dicarlo, J. J., & Maunsell, J. H. (2003). Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. J. Neurophysiol., 89, 3264-3278.
21. Dimitrov, A. G., Sheiko, M. A., Baker, J.,&Yen, S.C. (2009). Spatial and temporal jitter distort estimated functional properties of visual sensory neurons. J. Computational Neuroscience, 27, 309-319.
22. Dong, D.W., & Atick, J. J. (1995). Statistics of natural time-varying images. Network: Comput. Neural Syst., 6, 345-358.
23. Edelman, S. (1999). Representation and recognition in vision. Cambridge, MA: MIT Press.
24. Efron, B., & Tibshirani, R. J. (1998). An introduction to the bootstrap. London: Chapman and Hall.
25. Fairhall, A. L., Burlingame, C. A., Narasimhan, R., Harris, R. A., Puchalla, J. L., & Berry II, M. J. (2006). Selectivity formultiple stimulus features in retinal ganglion cells. J. Neurophysiol., 96, 2724-2738.
26. Felsen, G., Touryan, J.,Han, F., & Dan, Y. (2005). Cortical sensitivity to visual features in natural scenes. PLoS Biol., e342, 1819-1828.
27. Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A, 4, 2379-2394.
28. Field, G. D., Gauthier, J. L., Sher, A., Greschner, M., Machado, T., Shlens, J., et al. (2010). Functional connectivity in the retina at the resolution of photoreceptors. Nature, 467, 673-677.
29. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36, 193-202.
30. Gallant, J. L., Braun, J., & Van Essen, D. C. (1993). Selectivity for polar, hyperbolic, and Cartesian gratings in macaque visual cortex. Science, 259, 100-103.
31. Gallant, J. L., Connor, C. E., Rakshit, S., Lewis, J. W., & Van Essen, D. C. (1996). Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. J. Neurophysiol., 76, 2718-2739.
32. Gawne, T. J., & Martin, J.M. (2002). Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. J. Neurophysiol., 88, 1128-1135.
33. Gill, P., Zhang, J., Woolley, S. M., Fremouw, T., & Theunissen, F. E. (2006). Sound representation methods for spectrotemporal receptive field estimation. J. Comput. Neurosci., 21, 5-20.
34. Gross, C. G., Bender, D. B., & Rocha-Miranda, C. E. (1969). Visual receptive fields of neurons in the inferotemporal cortex of the monkey. Science, 166, 1303-1307.
35. Gross, C. G., Rocha-Miranda, C. E., & Bender, D. B. (1972). Visual properties of neurons in inferotemporal cortex of the macaque. J. Neurophysiol., 35, 96-111.
36. Heeger, D. J. (1992). Normalization of cell responses in cat visual cortex.Vis. Neurosci., 9, 181-197.
37. Hegde, J., & Van Essen, D. C. (2000). Selectivity for complex shapes in primate visual area V2. J. Neurosci., 20, 1-6.
38. Hegde, J., & Van Essen, D. C. (2007). A comparative study of shape representation in macaque visual areas V2 and V4. Cereb. Cortex, 17, 1100-1116.
39. Horwitz, G. D., Chichilnisky, E. J., & Albright, T. D. (2007). Cone inputs to simple and complex cells in V1 of awake macaque. J. Neurophysiol., 97, 3070-3081.
40. Ito, M., Tamura, H., Fujita, I., & Tanaka, K. (1995). Size and position invariance of neuronal responses in monkey inferotemporal cortex. J. Neurophysiol., 73, 218-226.
41. Kobatake, E., & Tanaka, K. (1994). Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. J. Neurophysiol., 71, 856-867.
42. Kouh, M., & Sharpee, T. O. (2009). Estimating linear-nonlinear models using Renyi divergences. Network: Comput. Neural Syst., 20, 49-68.
43. Leuschow, A., Miller, E. K., & Desimone, R. (1994). Inferior temporal mechanisms for invariant object recognition. Cereb. Cortex, 5, 523-531.
44. Liu, Y. S., Stevens, C. F., & Sharpee, T. O. (2009). Predictable irregularities in retinal receptive fields. PNAS, 106, 16499-16504.
45. Logothetis, N. K., Pauls, J., & Poggio, T. (1995). Shape representation in the inferior temporal cortex of monkeys. Curr. Biol., 5, 552-563.
46. Machens, C. K., Wehr, M. S., & Zador, A. M. (2004). Linearity of cortical receptive fields measured with natural sounds. J. Neurosci., 24, 1089-1100.
47. Mackay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Comput., 4, 590-604.
48. Mechler, F., & Ringach, D. L. (2002). On the classification of simple and complex cells. Vision Research, 42, 1017-1033.
49. Meister,M.,&Berry II, M. J. (1999). The neural code of the retina. Neuron, 22, 435-450.
50. Missal, M., Vogels, R., Li, C., & Orban, G. A. (1999). Shape interactions in macaque inferior temporal neurons. J. Neurophysiol., 82, 131-142.
51. Nishimoto, S., Ishida, T., & Ohzawa, I. (2006). Receptive field properties of neurons in the early visual cortex revealed by local spectral reverse correlation. J. Neurosci., 26, 3269-3280.
52. Op de Beeck,H., & Vogels, R. (2000). Spatial sensitivity of macaque inferior temporal neurons. J. Comp. Neurol., 426, 505-518.
53. Paninski, L. (2003). Convergence properties of three spike-triggered average techniques. Network: Comput. Neural Syst., 14, 437-464.
54. Pasupathy, A., & Connor, C. E. (1999). Responses to contour features in macaque area V4. J. Neurophysiol., 82, 2490-2502.
55. Pasupathy, A., & Connor, C. E. (2001). Shape representation in area V4: Positionspecific tuning for boundary conformation. J. Neurophysiol, 86, 2505-2519.
56. Pasupathy, A., & Connor, C. E. (2002). Population coding of shape in area V4. Nat. Neurosci., 5, 1332-1338.
57. Pelli, D. G. (1985). Uncertainty explains many aspects of visual cortex detection and discrimination. J. Opt. Soc. Am. A, 2, 1508-1532.
58. Pillow, J. W., & Simoncelli, E. P. (2006). Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision, 6, 414-428.
59. Pollen, D. A., Przybyszewski, A.W., Rubin, M. A., & Foote,W. (2002). Spatial receptive field organization of macaque V4 neurons. Cereb. Cortex, 12, 601-616.
60. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical recipes in C: The art of scientific computing. Cambridge: Cambridge University Press.
61. Rainer, G., Augath, M., Trinath, T., & Logothetis, N. K. (2001). Nonmonotonic noise tuning of bold fMRI signal to natural images in the visual cortex of the anesthetized monkey. Curr. Biol., 11, 846-854.
62. Rapela, J., Felsen, G., Touryan, J., Mendel, J. M., & Grzywacz, N. M. (2010). ePPR: A new strategy for the characterization of sensory cells from input/output data. Network: Computation in Neural Systems, 21, 35-90.
63. Rapela, J., Mendel, J. M., & Grzywacz, N. M. (2006). Estimating nonlinear receptive fields from natural images. Journal of Vision, 16, 441-474.
64. Richmond, B. J.,Wurtz, R. H., & Sato, T. (1983). Visual responses of inferior temporal neurons in awake rhesus monkey. J. Neurophysiol., 50, 1415-1432.
65. Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1997). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.
66. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nat. Neurosci, 2, 1019-1025.
67. Ringach, D. L., Hawken, M. J., & Shapley, R. (2002). Receptive field structure of neurons in monkey visual cortex revealed by stimulation with natural image sequences. Journal of Vision, 2, 12-24.
68. Ringach, D. L., Sapiro, G., & Shapley, R. (1997). A subspace reverse-correlation technique for the study of visual neurons. Vision Res., 37, 2455-2464.
69. Rolls, E. T. (2000). Functions of the primate temporal lobe cortical visual areas in invariant-object recongnition. Neuron, 27, 205-218.
70. Rowekamp, R. J.,&Sharpee, T. O. (2011).Analyzing multicomponent receptive fields from neural responses to natural stimuli. Network: Comput. Neural Syst., 22, 45-73.
71. Ruderman, D. L., & Bialek, W. (1994). Statistics of natural images: Scaling in the woods. Phys. Rev. Lett., 73, 814-817.
72. Rust, N. C., & Dicarlo, J. J. (2010). Selectivity and tolerance ("invariance") both increase as visual information propagates fromcortical areaV4 to IT. J. Neuroscience, 30, 12978-12995.
73. Rust, N. C., Schwartz, O., Movshon, J. A., & Simoncelli, E. P. (2005). Spatiotemporal elements of macaque V1 receptive fields. Neuron, 46, 945-956.
74. Sahani, M., & Linden, J. F. (2003). How linear are auditory cortical responses? In S. Becker, S. Thrün, & K. Obermayer (Eds.), Advances in neural information processing systems, 15 (pp. 109-116). Cambridge, MA: MIT Press.
75. Saleem, K. S., Tanaka, K., & Rockland, K. S. (1993). Specific and columnar projection from area TEO to TE in the macaque inferotemporal cortex. Cereb. Cortex, 3, 454-464.
76. Sary, G., Vogels, R.,& Orban,G. A. (1993).Cue-invariant shape selectivity of macaque inferior temporal neurons. Science, 260, 995-997.
77. Schwartz, E. L., Desimone,R., Albright, T.D.,&Gross, C. G. (1983). Shape recognition and inferior temporal neurons. Proc. Natl. Acad. Sci. USA, 80, 5776-5778.
78. Schwartz, O., Chichilnisky, E. J., & Simoncelli, E. (2002). Characterizing neural gain control using spike-triggered covariance. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing, 14. Cambridge, MA: MIT Press.
79. Schwartz, O., Pillow, J., Rust, N., & Simoncelli, E. P. (2006). Spike-triggered neural characterization. Journal of Vision, 6, 484-507.
80. Schwartz, O., & Simoncelli, E. P. (2001). Natural signal statistics and sensory gain control. Nat. Neurosci., 4, 819-825.
81. Sharpee, T. O. (2007). Comparison of information and variance maximization strategies for characterizing neural feature selectivity. Statistics in Medicine, 26, 4009-4031.
82. Sharpee, T. O., Miller, K. D., & Stryker, M. P. (2008). On the importance of static nonlinearity in estimating spatiotemporal neural filters with natural stimuli. J. Neurophysiol., 99, 2496-2509.
83. Sharpee, T., Rust, N., & Bialek, W. (2004). Analyzing neural responses to natural signals: Maximally informative dimensions. Neural Comput., 16, 223-250.
84. Sharpee, T. O., Sugihara, H.,Kurgansky, A.V., Rebrik, S. P., Stryker, M. P.,& Miller, K. D. (2006). Adaptive filtering enhances information transmission in visual cortex. Nature, 439, 936-942.
85. Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annu. Rev. Neurosci., 24, 1193-1216.
86. Sincich, L. C.,Horton, J. C., & Sharpee, T. O. (2009). Preserving information in neural transmission. J. Neurosci., 29, 6207-6216.
87. Skottun, B. C., De Valois, R. L., Grosof, D. H., Movshon, J. A., Albrecht, D. G., & Bonds, A. B. (1991). Classifying simple and complex cells on the basis of response modulation. Vision Res., 31, 1079-1086.
88. Soo, F. S., Schwartz, G. W., Sadeghi, K., & Berry II, M. (2011). Fine spatial information represented in a population of retinal ganglion cells. J. Neurosci., 31, 2145-2155.
89. Soodak, R. E., Shapley, R. M., & Kaplan, E. (1991). Fine structure of receptive field centers of X and Y cells of the cat. Visual Neuroscience, 6, 621-628.
90. Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R.,& Bialek,W. (1998). Entropy and information in neural spike trains. Phys. Rev. Lett., 80, 197-200.
91. Theunissen, F. E., David, S. V., Singh, N. C., Hsu, A., Vinje, W. E., & Gallant, J. L. (2001). Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network, 3, 289-316.
92. Theunissen, F. E., Sen, K., & Doupe, A. J. (2000). Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J. Neurosci., 20, 2315-2331.
93. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520-522.
94. Tjan, B. S., & Nandy, A. S. (2006). Classification images with uncertainty. Journal of Vision, 6, 387-413.
95. Touryan, J., Felsen, G., & Dan, Y. (2005). Spatial structure of complex cell receptive fields measured with natural images. Neuron, 45, 781-791.
96. Touryan, J., Lau, B., & Dan, Y. (2002). Isolation of relevant visual features from random stimuli for cortical complex cells. J. Neurosci., 22, 10811-10818.
97. Tovee, M. J., Rolls, E. T., & Azzopardi, P. (1994). Translation invariance in the responses to faces of single neurons in the temporal visual cortical areas of the alert macaque. J. Neurophysiol., 72, 1049-1060.
98. Treves, A., & Panzeri, S. (1995). The upward bias in measures of information derived from limited data samples. Neural Comp., 7, 399-407.
99. Ullman, S. (1996). High level vision. Cambridge, MA: MIT Press.
100. van Hateren, J. H., & Ruderman, D. L. (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 Biol. Sci., 265, 2315-2320.
101. Victor, J. D., & Shapley, R. (1980). A method of nonlinear analysis in the frequency domain. Biophys. J., 29, 456-483.
102. Wang, G., Tanaka, K., & Tanifuji, M. (1996). Optical imaging of functional organization in the monkey inferotemporal cortex. Science, 272, 1665-1668.
103. Wang, G., Tanifuji, M., & Tanaka, K. (1998). Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging. Neurosci. Res., 32, 33-46.
104. Woolley, S. M. N., Fremouw, T. E., Hsu, A., & Theunissen, F. E. (2005). Tuning for spectro-temporal modulations as a mechanism for auditory discrimination of natural sounds. Nat. Neurosci., 8(10), 1371-1379.
105. Woolley, S. M. N., Gill, P. R.,& Theunissen, F. E. (2006). Stimulus-dependent auditory tuning results in synchronous population coding of vocalization in the songbird midbrain. J. Neurosci., 26, 2499-2512.
106. Zoccolan, D., Kouh, M., Poggio, T., & Dicarlo, J. J. (2007). Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. J. Neurosci., 27, 12292-12307.