Linking Perception and Neurophysiology for Motion Pattern Processing: The Computational Power of Inhibitory Connections in Cortex

  • Scott A. Beardsley
  • Lucia M. Vaina
Chapter
Part of the Synthese Library book series (SYLI, volume 324)

Abstract

The motion of the visual scene across the retina, termed optic flow (Gibson, 1950), contains a wealth of information about our dynamic relationship within the environment. Perceptual information regarding heading, time to contact, object motion and object segmentation can all be recovered to various degrees by analyzing the complex motion components of optic flow; for review see (Andersen, 1997, Lappe, et al., 1999). While the usefulness of such information for visually guided actions and navigation is clear, the complex neural mechanisms underlying its processing and extraction remain, for the most part, poorly understood.

Keywords

Anisotropy Retina Rosen 

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References

  1. Adini, Y., Sagi, D., and Tsodyks, M. (1997). Excitatory-inhibitory network in the visual cortex: Psychophysical evidence. Proc. Natl. Acad. Sci., 94, 1 0426–1 043 1.Google Scholar
  2. Amir, Y., Harel, M., and Malach, R. (1993). Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex. J. Comp. Neurology, 334, 19–46.CrossRefGoogle Scholar
  3. Andersen, R. A. (1997). Neural mechanisms of visual motion perception in primates. Neuron, 18, 865–872.PubMedCrossRefGoogle Scholar
  4. Andersen, R. A., Shenoy, K. V., Crowell, J. A., & Bradley, D. C. (2000). Neural Mechanisms for Self-Motion Perception in Area MST. In: M. Lappe (Ed.). Neuronal Processing of Optic Flow, 44 (pp. 219–234 ). New York: Academic Press.CrossRefGoogle Scholar
  5. Ball, K., & Sekuler, R. (1987). Direction-specific improvement in motion discrimination. Vision Res., 27 (6), 953–965.PubMedCrossRefGoogle Scholar
  6. Beardsley, S. A., & Vaina, L. M. (1998). Computational modeling of optic flow selectivity in MSTd neurons. Network: Comput. Neural Syst., 9, 467–493.Google Scholar
  7. Beardsley, S. A., & Vaina, L. M. (2001). A laterally interconnected neural architecture in MST accounts for psychophysical discrimination of complex motion patterns. J. Comput. Neurosci., 10, 255–280.PubMedCrossRefGoogle Scholar
  8. Beardsley, S. A., Ward, R. L., & Vaina, L. M. (2003). A feed-forward network model of spiral-planar tuning in MSTd. Vision Res., 43, 577–595.Google Scholar
  9. Ben-Yishai, B., Bar-Or, R. L., & Sompolinsky, H. (1995). Theory of orientation tuning in visual cortex. Proc. Natl. Acad. Sci., 92, 3844–3848.PubMedCrossRefPubMedCentralGoogle Scholar
  10. Bevington, P. (1969). Data Reduction and Error Analysis for the Physical Sciences, (p. 336 ). New York: McGraw-Hill.Google Scholar
  11. Boussaoud, D., Ungerleider, L. G., & Desimone, R. (1990). Pathways for motion analysis: cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque. J. Comp. Neurol., 296 (3), 462–495.PubMedCrossRefGoogle Scholar
  12. Bremmer, F., Duhamel, J.-R., Ben Hamed, S., & Werner, G. (2000). Stages of Self-Motion Processing in Primate Posterior Parietal Cortex. In: M. Lappe (Ed.) Neuronal Processing of Optic Flow, 44 (pp. 173–198 ). New York: Academic Press.CrossRefGoogle Scholar
  13. Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., & Movshon, J. A. (1996). A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci., 13 (1), 87–100.Google Scholar
  14. 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. J Neurosci., 12 (12), 4745–4765.Google Scholar
  15. Britten, K. H., & van Wezel, R. J. A. (1998). Electrical microstimulation of cortical area MST biases heading perception in monkeys. Nat. Neurosci., 1, 59–63.PubMedCrossRefGoogle Scholar
  16. Burr, D. C., Morrone, M. C., & Vaina, L. M. (1998). Large receptive fields for optic flow detection in humans. Vision Res., 38 (12), 1731–1743.Google Scholar
  17. Carandini, M., & Ringach, D. L. (1997). Prediction of a recurrent model of orientation selectivity. Vision Res., 37, 3061–3071.PubMedCrossRefGoogle Scholar
  18. Celebrini, S., & Newsome, W. T. (1994). Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. J. Neurosci., 14 (7), 4109–4124.Google Scholar
  19. Celebrini, S., & Newsome, W. T. (1995). Microstimulation of extrastriate area MST influences performance on a direction discrimination task. J. Neurphysiol., 73 (2), 437–448.Google Scholar
  20. Chey, J., Grossberg, S., & Mingolla, E. (1998). Neural dynamics of motion processing and speed discrimination. Vision Res., 38, 2769–2786.PubMedCrossRefGoogle Scholar
  21. Coletta, N. J., Segu, P., & Tiana, C. L. (1993). An oblique effect in parafovial motion perception. Vision Res., 33 (18), 2747–2756.Google Scholar
  22. de Jong, B. M., Shipp, S., Skidmore, B., Frackowiak, R. S. J., & Zeki, S. (1994). The cerebral activity related to the visual perception of forward motion in depth. Brain, 117, 1039–1054.PubMedCrossRefGoogle Scholar
  23. deCharms, R. C., & Zador, A. (2000). Neural representation and the cortical code. Annu. Rev. Neurosci., (23), 613–647.Google Scholar
  24. DeYoe, E. A., & Van Essen, D. C. (1988). Concurrent processing streams in monkey visual cortex. Trends Neurosci., 11 (5), 219–226.PubMedCrossRefGoogle Scholar
  25. Duffy, C. J. (2000). Optic Flow Analysis for Self-Movement Perception. In: M. Lappe (Ed.) Neuronal Processing of Optic Flow, 44 (pp. 199–218 ). New York: Academic Press.CrossRefGoogle Scholar
  26. Duffy, C. J., & Wurtz, R. H. (1991a). Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. J. Neurophysiol., 65 (6), 1329–1345.Google Scholar
  27. Duffy, C. J., & Wurtz, R. H. (1991b). Sensitivity of MST neurons to optic flow stimuli. II. Mechanisms of response selectivity revealed by small field stimuli. J. Neurophysiol., 65 (6), 1346–1359.Google Scholar
  28. Duffy, C. J., & Wurtz, R. H. (1995). Response of monkey MST neurons to optic flow stimuli with shifted centers of motion. J. Neurosci., 15 (7), 5192–5208.Google Scholar
  29. Duffy, C. J., & Wurtz, R. H. (1997a). Medial superior temporal area neurons respond to speed patterns in optic flow. J. Neurosci., 17 (8), 2839–2851.Google Scholar
  30. Duffy, C. J., & Wurtz, R. H. (1997b). Planar directional contributions to optic flow responses in MST neurons. J. Neurophysiol., 77, 782–796.PubMedGoogle Scholar
  31. Edelman, S. (1996). Why Have Lateral Connections in the Visual Cortex? In: J. Sirosh, R. Miikkulainen, & Y. Choe (Eds.), Lateral Interactions in the Cortex: Structure and Function, Electronic Book (http://www.cs.utexas.edu/users/nn/webpubs/htmlbook96/edelman/). Austin: The UTCS Neural Networks Research Group.Google Scholar
  32. Edwards, M., & Badcock, D. R. (1993). Asymmetries in the sensitivity to motion in depth: a centripetal bias. Perception, 22, 1013–1023.PubMedCrossRefGoogle Scholar
  33. Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex, 1, 1–47.PubMedCrossRefGoogle Scholar
  34. Foldiak, P. (1993). The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses. In: F.H. Eeckman, & J.M. Bower (Eds.), Computation and Neural Systems (pp. 55–60 ). Norwell: Kluwer Academic Publishers.CrossRefGoogle Scholar
  35. Freeman, T. C., & Harris, M. G. (1992). Human sensitivity to expanding and rotating motion: Effects of complementary masking and directional structure. Vision Res., 32 (1), 81–87.Google Scholar
  36. Geesaman, B. J., & Andersen, R. A. (1996). The analysis of complex motion patterns by form/cue invariant MSTd neurons. J. Neurosci., 16 (15), 4716–4732.Google Scholar
  37. Georgopoulos, A. P., Kettner, R. E., & Schwartz, A. B. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci., 8 (8), 2928–2937.Google Scholar
  38. Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science, 233 (26), 1416–1419.PubMedCrossRefGoogle Scholar
  39. Gibson, J. J. (1950). The Perception of the Visual World. ( Boston: Houghton Mifflin).Google Scholar
  40. Gilbert, C., Das, A., Ito, M., Kapadia, M., & Westheimer, G. (1996). Spatial integration and cortical dynamics. Proc. Natl. Acad. Sci. USA, 93, 615–622.PubMedCrossRefPubMedCentralGoogle Scholar
  41. Gilbert, C., & Wiesel, T. (1989). Columnar specificity of instrinsic horizontal and corticocortical connections in cat visual cortex. J. Neurosci., 9 (7), 2432–2442.Google Scholar
  42. Gilbert, C. D. (1985). Horizontal integration in the neocortex. Trends Neurosci., (April), 160–165.Google Scholar
  43. Gilbert, C. D. (1992). Horizontal integration and cortical dynamics. Neuron, 9, 1–13.PubMedCrossRefGoogle Scholar
  44. Gilbert, C. D., & Wiesel, T. N. (1985). Intrisic connectivity and receptive field properties in visual cortex. Vision Res., 25 (3), 365–374.Google Scholar
  45. Graziano, M. S., Anderson, R. A., & Snowden, R. (1994). Tuning of MST neurons to spiral motions. J. Neurosci., 14 (1), 54–67.Google Scholar
  46. Greenlee, M. W. (2000). Human cortical areas underlying the perception of optic flow: brain imaging studies. Int. Rev. Neurobiol., 44, 269–292.PubMedCrossRefGoogle Scholar
  47. Grinvald, A., Lieke, E., Frostig, R. D., Gilbert, C. D., & Wiesel, T. N. (1986). Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature, 324, 361–364.PubMedCrossRefGoogle Scholar
  48. Gros, B. L., Blake, R., & Hiris, E. (1998). Anisotropies in visual motion perception: a fresh look. J. Opt. Soc. Am. A, 15 (8), 2003–2011.Google Scholar
  49. Grossberg, S., Mignolla, E., & Pack, C. (1999). A neural model of motion processing and visual navigation by cortical area MST. Cereb. Cortex, 9 (8), 878–895.Google Scholar
  50. Grossberg, S., & Williamson, J. R. (2001). A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual grouping and learning. Cereb. Cortex, 11, 37–58.PubMedCrossRefGoogle Scholar
  51. Hatsopoulos, N., & Warren, W. J. (1991). Visual navigation with a neural network. Neural Netw., 4, 303–317.CrossRefGoogle Scholar
  52. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, (p. 842 ). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  53. Heeger, D. J. (1999). Linking visual perception with human brain activity. Curr. Opin. Neurobiol., 9 (4), 474–479.Google Scholar
  54. Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the Theory of Neural Computation. A Lecture Notes Volume in the Santa Fe Institute Studies in the Sciences of Complexity (p. 327 ). New York: Addison-Wesley Publishing Company.Google Scholar
  55. Kalaska, J. F., Caminiti, R., & Georgopoulos, A. P. (1983). Cortical mechanisms related to the direction of two-dimensional arm movements: relations in parietal area 5 and comparison with motor cortex. Exp. Brain Res., 51, 247–260.PubMedCrossRefGoogle Scholar
  56. Kisvarday, Z., Toth, E., Rausch, M., & Eysel, U. (1997). Orientation-specific relationship between populations of excitatory and inhibitory lateral connections in the visual cortex of the cat. Cereb. Cortex, 7 (7), 605–618.Google Scholar
  57. Koechlin, E., Anton, J., & Burnod, Y. (1999). Bayesian interference in populations of cortical neurons: A model of motion integration and segmentation in area MT. Biol. Cybern., 80, 25–44.PubMedCrossRefGoogle Scholar
  58. Lagae, L., Maes, H., Raiguel, S., Xiao, D.-K., & Orban, G. A. (1994). Responses of macaque STS neurons to optic flow components: a comparison of areas MT and MST. J. Neurophysiol., 71 (5), 1597–1626.Google Scholar
  59. Lappe, M. (2000). Computational mechanisms for optic flow analysis in primate cortex. In: M. Lappe (Ed.) Neuronal Processing of Optic Flow, 44 (pp. 235–268 ). New York: Academic Press.CrossRefGoogle Scholar
  60. Lappe, M., Bremmer, F., Pekel, M., Thiele, A., & Hoffmann, K. (1996). Optic flow processing in monkey STS: A theoretical and experimental approach. J. Neurosci., 16 (19), 6265–6285.Google Scholar
  61. Lappe, M., Bremmer, F., & van den Berg, A. V. (1999). Perception of self-motion from visual flow. Trends Cogn. Sci., 3 (9), 329–336.PubMedCrossRefGoogle Scholar
  62. Lappe, M., & Duffy, C. (1999). Optic flow illusion and single neuron behavior reconciled by a population model. Eur. J. Neurosci., 11, 2323–2331.PubMedCrossRefGoogle Scholar
  63. Lappe, M., & Rauschecker, J. P. (1993). A neural network for the processing of optic flow from ego-motion in man and higher mammals. Neural Comput., 5, 374–391.CrossRefGoogle Scholar
  64. Lappe, M., & Rauschecker, J. P. (1995). Motion anisotropies and heading detection. Biol. Cybern., 72 (3), 261–277.Google Scholar
  65. Liu, L., & Hulle, V. (1998). Modeling the surround of MT cells and their selectivity for surface orientation in depth specified by motion. Neural Comput., 10, 295–312.PubMedCrossRefGoogle Scholar
  66. Lukashin, A. V., & Georgopoulos, A. P. (1993). A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories. Biol. Cybern., 69, 517–524.PubMedCrossRefGoogle Scholar
  67. Lukashin, A. V., & Georgopoulos, A. P. (1994). A neural network for coding of trajectories by time series of neuronal population vectors. Neural Comput., 6, 19–28.CrossRefGoogle Scholar
  68. Lukashin, A. V., Wilcox, G. L., & Georgopoulos, A. P. (1996). Modeling of directional operations in the motor cortex: a noisy network of spiking neurons is trained to generate a neural-vector trajectory. Neural Netw., 9 (3), 397–410.Google Scholar
  69. Lund, J., Yoshioka, T., & Levitt, J. (1993). Comparison of intrinsic connectivity in different areas of the macaque monkey cerebral cortex. Cereb. Cortex, 3 (2), 148–162.Google Scholar
  70. Malach, R., Schirman, T., Harel, M., Tootell, R., & Malonek, D. (1997). Organization of intrinsic connections in owl monkey area MT. Cereb. Cortex, 7 (4), 386–393.Google Scholar
  71. Matthews, N., & Qian, N. (1999). Axis-of-motion affects direction discrimination, not speed discrimination. Vision Res., 39, 2205–2211.PubMedCrossRefGoogle Scholar
  72. Matthews, N., & Welch, L. (1997). Velocity-dependent improvements in single-dot direction discrimination. Percept. Psychophys., 59 (1), 60–72.Google Scholar
  73. Maunsell, J. H., & Van Essen, D. C. (1983). The connections of the middle temporal visual area (MT) and their relationship to a cortical heirarchy in the macaque monkey. J. Neurosci., 3 (12), 2563–2586.Google Scholar
  74. McGuire, B., Gilbert, C., Rivlin, P., & Wiesel, T. (1991). Targets of horizontal connections in macaque primary visual cortex. J. Comp. Neurol., 305, 370–392.PubMedCrossRefGoogle Scholar
  75. Meese, T., & Harris, M. (2001a). Independent detectors for expansion and rotation, and for orthogonal components of deformation. Perception, 30, 1189–1202.PubMedCrossRefGoogle Scholar
  76. Meese, T. S., & Harris, M. G. (2001b). Broad direction bandwidths for complex motion mechanisms. Vision Res, 41 (15), 1901–1914.PubMedCrossRefGoogle Scholar
  77. Meese, T. S., & Harris, S. J. (2002). Spiral mechanisms are required to account for summation of complex motion components. Vision Res., 42, 1073–1080.PubMedCrossRefGoogle Scholar
  78. Miikkulainen, R., and Sirosh, J. (1996). Introduction: The Emerging Understanding of Lateral Interactions in the Cortex. In: J. Sirosh, R. Miikkulainen, & Y. Choe (Eds.), Lateral Interactions in the Cortex: Structure and Function, Electronic Book (http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96/miikkulainen/). Austin: The UTCS Neural Networks Research Group.Google Scholar
  79. Morrone, C., Burr, D., & Vaina, L. (1995). Two stages of visual processing for radial and circular motion. Nature, 376, 507–509.PubMedCrossRefGoogle Scholar
  80. Morrone, M. C., Burr, D. C., Di Pietro, S., & Stefanelli, M. A. (1999). Cardinal directions for visual optic flow. Curr. Biol., 9, 763–766.PubMedCrossRefGoogle Scholar
  81. Morrone, M. C., Tosetti, M., Montanaro, D., Fiorentini, A., Cioni, G., & Burr, D. C. (2000). A cortical area that responds specifically to optic flow, revealed by fMRI. Nat. Neurosci., 3 (12), 1322–1328.Google Scholar
  82. Nowlan, S. J., and Sejnowski, T. J. (1995). A selection model for motion processing in area MT of primates. J. Neurosci., 15 (2), 1195–1214.Google Scholar
  83. Oram, M. W., Foldiak, P., Perrett, D. I., & Sengpiel, F. (1998). The ‘ideal homunculus’: decoding neural population signals. Trends Neurosci., 21 (8), 365–371.Google Scholar
  84. Orban, G. A., Lagae, L., Raiguel, S., Xiao, D., & Maes, H. (1995). The speed tuning of medial superior temporal (MST) cell responses to optic-flow components. Perception, 24 (3), 269–285.Google Scholar
  85. Orban, G. A., Lagae, L., Verri, A., Raiguel, S., Xiao, D., Maes, H., & Torre, V. (1992). First-order analysis of optical flow in monkey brain. Proc. Natl. Acad. Sci. USA, 89, 2595–2599.PubMedCrossRefPubMedCentralGoogle Scholar
  86. Perrone, J., & Stone, L. (1994). A model of self-motion estimation within primate extrastriate visual cortex. Vision Res., 34 (21), 2917–2938.Google Scholar
  87. Perrone, J. A., & Stone, L. S. (1998). Emulating the visual receptive-field properties of MST neurons with a template model of heading estimation. J. Neurosci., 18 (15), 5958–5975.Google Scholar
  88. Pitts, R. I., Sundareswaran, V., & Vaina, L. M. (1997). A model of position-invariant, optic flow pattern-selective cells. In: Computational Neuroscience: Trends in Research 1997 (pp. 171–176 ). New York: Plenum Publishing Corporation.CrossRefGoogle Scholar
  89. Pouget, A., Zhang, K., Deneve, S., & Latham, P. E. (1998). Statistically efficient estimation using population coding. Neural Comput., 10, 373–401.PubMedCrossRefGoogle Scholar
  90. Raymond, J. E. (1994). Directional anisotropy of motion sensitivity across the visual field. Vision Res., 34 (8), 1029–1039.Google Scholar
  91. Rees, G., Friston, K., & Koch, C. (2000). A direct quantative relationship between the function properties of human and macaque V5. Nat. Neurosci., 3 (7), 716–723.Google Scholar
  92. Regan, D., & Beverley, K. (1978). Looming detectors in the human visual pathway. Vision Res., 18, 415–421.PubMedCrossRefGoogle Scholar
  93. Regan, D., & Beverley, K. I. (1979). Visually guided locomotion: Psychophysical evidence for a neural mechanism sensitive to flow patterns. Science, 205, 311–313.PubMedCrossRefGoogle Scholar
  94. Royden, C. S. (1997). Mathematical analysis of motion-opponent mechanisms used in the determination of heading and depth. J. Opt. Soc. Am. A, 14, 2128–2143.CrossRefGoogle Scholar
  95. Rutschmann, R. M., Schrauf, M., & Greenlee, M. W. (2000). Brain activation during dichoptic presentation of optic flow stimuli. Exp. Brain Res., 134, 533–537.PubMedCrossRefGoogle Scholar
  96. Saito, H.-a., Yukie, M., Tanaka, K., Hikosaka, K., Fukada, Y., & Iwai, E. (1986). Integration of direction signals of image motion in the superior temporal sulcus of the macaque monkey. J. Neurosci., 6 (1), 145–157.Google Scholar
  97. Sakai, K., & Miyashita, Y. (1991). Neural organization for the long-term memory of paired associates. Nature, 354, 152–155.PubMedCrossRefGoogle Scholar
  98. Salinas, E., & Abbott, L. (1994). Vector reconstruction from firing rates. J. Comput. Neurosci., 1, 89–107.PubMedCrossRefGoogle Scholar
  99. Salinas, E., & Abbott, L. (1995). Transfer of coded information from sensory to motor networks. J. Neurosci., 15, 6461–6474.PubMedGoogle Scholar
  100. Salzman, C. D., Britten, K. H., & Newsome, W. T. (1990). Cortical microstimulation influences perceptual judgements of motion direction. Nature, 346, 174–177.PubMedCrossRefGoogle Scholar
  101. Salzman, C. D., Murasugi, C. M., Britten, K., & Newsome, W. T. (1992). Microstimulation in visual area MT: effects on direction discrimination performance. J. Neurosci., 12 (6), 2331–2355.Google Scholar
  102. Sanger, T. D. (1996). Probability density estimation for the interpretation of neural population codes. J. Neurophysiol., 76 (4), 2790–2793.Google Scholar
  103. Schaafsma, S. J., & Duysens, J. (1996). Neurons in the ventral intraparietal area of awake macaque monkey closely resemble neurons in the dorsal part of the medial superior temporal area in their responses to optic flow patterns. J. Neurophysiol., 76 (6), 4056–4068.Google Scholar
  104. Schwartz, A. B., Kettner, R. E., & Georgopoulos, A. P. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement. J. Neurosci., 8 (8), 2913–2827.Google Scholar
  105. Seung, H. S., & Sompolinsky, H. (1993). Simple models for reading neuronal population codes. Proc. Natl. Acad. Sci. USA, 90, 10749–10753.PubMedCrossRefPubMedCentralGoogle Scholar
  106. Shadlen, M., & Newsome, W. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci., 18 (10), 3870–3896.Google Scholar
  107. Shadlen, M. N., & Newsome, W. T. (1994). Noise, neural codes, and cortical organization. Curr. Opin. Neurobiol., 4, 569–579.PubMedCrossRefGoogle Scholar
  108. Siegel, R. M., & Read, H. L. (1997). Analysis of optic flow in the monkey parietal area 7a. Cereb. Cortex, 7 (4), 327–346.Google Scholar
  109. Snippe, H. (1996). Parameter extraction from population codes: A critical assessment. Neural Comput., 8, 511–530.PubMedCrossRefGoogle Scholar
  110. Snowden, R. J., & Milne, A. B. (1996). The effects of adapting to complex motions: position invariance and tuning to spiral motions. J. Cognit. Neurosci., 8 (4), 412–429.Google Scholar
  111. Snowden, R. J., & Milne, A. B. (1997). Phantom motion aftereffects–evidence of detectors for the analysis of optic flow. Curr. Biol., 7, 717–722.PubMedCrossRefGoogle Scholar
  112. Softky, W. (1995). Simple codes versus efficient codes. Curr. Opin. Neurobiol., 5, 239–247.PubMedCrossRefGoogle Scholar
  113. Softky, W., & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci., 13 (1), 334–350.Google Scholar
  114. Stemmler, M., Usher, M., & Niebur, E. (1995). Lateral interactions in primary visual cortex: A model bridging physiology and psychophysics. Science, 269, 1877–1880.PubMedCrossRefGoogle Scholar
  115. Sundareswaran, V., & Vaina, L. M. (1996). Adaptive computational models of fast learning of motion direction discrimination. Biol. Cybern., 74, 319–329.PubMedCrossRefGoogle Scholar
  116. Tanaka, K., Fukada, Y., & Saito, H.-A. (1989). Underlying mechanisms of the response specificity of expansion/contraction and rotation cells in the dorsal part of the medial superior temporal area of the macaque monkey. J. Neurophysiol., 62 (3), 642–656.Google Scholar
  117. Tanaka, K., & Saito, H.-A. (1989). Analysis of motion of the visual field by direction, expansion/contraction, and rotation cells clustered in the dorsal part of the medial superior temporal area of the macaque monkey. J. Neurophysiol., 62 (3), 626–641.Google Scholar
  118. Taylor, J. G., & Alavi, F. N. (1996). A Basis for Long-Range Inhibition Across Cortex. In: J. Sirosh, R. Miikkulainen, & Y. Choe (Eds.), Lateral Interactions in the Cortex: Structure and Function, Electronic Book (http://www.cs.utexas.edu/users/nn/webpubs/htmlbook96/taylor/). Austin: The UTCS Neural Networks Research Group.Google Scholar
  119. Te Pas, S. F., Kappers, A. M., & Koenderink, J. J. (1996). Detection of first-order structure in optic flow fields. Vision Res., 36 (2), 259–270.Google Scholar
  120. Teich, A. F., & Qian, N. (2002). Learning and adaptation in a recurrent model of V1 orientation selectivity. J. Neurophysiol., in press.Google Scholar
  121. Tootell, R. B. H., Reppas, J. B., Kwong, K. K., Malach, R., Born, R. T., Brady, T. J., Rosen, B. R., & Belliveau, J. W. (1995). Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. J. Neurosci., 15 (4), 3215–3230.Google Scholar
  122. Ts’o, D., Gilbert, C. D., & Wiesel, T. N. (1986). Relationships between horizontal interactions and functional architecture in cat striate cortex as revealed by cross-correlation analysis. J. Neurosci., 6 (4), 1160–1170.Google Scholar
  123. Vaina, L. M. (1998). Complex motion perception and its deficits. Curr. Opin. Neurobiol., 8 (4), 494–502.Google Scholar
  124. Vaina, L. M., Solovyev, S., Kopcik, M., & Chowdhury, S. (2000). Impaired self-motion perception from optic flow: a psychophysical and fMRI study of a patient with a left occipital lobe lesion. Soc. Neurosci. Abst., 26, 1065.Google Scholar
  125. Vaina, L. M., Sundareswaran, V., & Harris, J. G. (1995). Learning to ignore: psychophysics and computational modeling of fast learning of direction in noisy motion stimuli. Cognit. Brain Res, 2 (3), 155–163.CrossRefGoogle Scholar
  126. van den Berg, A. V. (2000). Human Ego-Motion Perception. In: M. Lappe (Ed.) Neuronal Processing of Optic Flow, 44 (pp. 3–25 ). New York: Academic Press.CrossRefGoogle Scholar
  127. Van Essen, D. C., & Maunsell, J. H. R. (1983). Hierarchical organization and functional streams in the visual cortex. Trends Neurosci., 6 (9), 370–375.Google Scholar
  128. Wang, R. (1995). A simple competitive account of some response properties of visual neurons in area MSTd. Neural Comput., 7, 290–306.Google Scholar
  129. Wang, R. (1996). A network model for the optic flow computation of the MST neurons. Neural Netw., 9 (3), 411–426.Google Scholar
  130. Wiskott, L., & von der Malsburg, C. (1996). Face Recognition by Dynamic Link Matching. In: J. Sirosh, R. Miikkulainen, & Y. Choe (Eds.), Lateral Interactions in the Cortex: Structure and Function, Electronic Book (http://www.cs.utexas.edu/users/nn/webpubs/htmlbook96/wiskott/). Austin: The UTCS Neural Networks Research Group.Google Scholar
  131. Worgotter, F., Niebur, E., & Christof, K. (1991). Isotropic connections generate functional asymmetrical behavior in visual cortical cells. J. Neurophysiol, 66 (2), 444–459.Google Scholar
  132. Zemel, R., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Comput., 10, 403–430.Google Scholar
  133. Zemel, R. S., & Sejnowski, T. J. (1998). A model for encoding multiple object motions and self-motion in area MST of primate visual cortex. J. Neurosci., 18 (1), 531–547.Google Scholar
  134. Zhang, K., Sereno, M. I., & Sereno, M. E. (1993). Emergence of position-independent detectors of sense of rotation and dilation with Hebbian learning: an analysis. Neural Comput., 5, 597–612.CrossRefGoogle Scholar
  135. Zhao, L., Vaina, L. M., LeMay, M., Kader, B., Chou, I. S., & Kemper, T. (1995). Are there specific anatomical correlates of global motion perception in the human visual cortex? Invest. Ophthalmol. Vis. Sci., 36 (4), S56.Google Scholar
  136. Zohary, E. (1992). Population coding of visual stimuli cortical neurons tuned to more than one dimension. Biol. Cybern., 66, 265–272.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2004

Authors and Affiliations

  • Scott A. Beardsley
    • 1
  • Lucia M. Vaina
    • 1
    • 2
  1. 1.Brain and Vision Research Laboratory, Department of Biomedical EngineeringBoston UniversityBostonUSA
  2. 2.Department of NeurologyHarvard Medical SchoolBostonUSA

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