Computing with a canonical neural circuits model with pool normalization and modulating feedback

Neural Computation

Volumn 26, Issue 12, 2014, Pages 2735-2789

  Brosch, Tobias ^a   Neumann, Heiko ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; COMPUTER SIMULATION; FEEDBACK SYSTEM; HUMAN; NERVE CELL; NERVE CELL NETWORK; NERVE TRACT; NONLINEAR SYSTEM; PHOTOSTIMULATION; PHYSIOLOGY; VISION;

ALGORITHMS; COMPUTER SIMULATION; FEEDBACK; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; NEURAL PATHWAYS; NEURONS; NONLINEAR DYNAMICS; PHOTIC STIMULATION; VISUAL PERCEPTION;

EID: 84910675817     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00675     Document Type: Article

References (163)

1. Abbott, L. F., & Chance, F. S. (2005). Drivers and modulators from push-pull and balanced synaptic input. Progress in Brain Research, 149, 147-155.
2. Ahmadian, Y., Rubin, D. B., & Miller, K. D. (2013). Analysis of the stabilized supralinear network. Neural Computation, 25(8), 1994-2037.
3. Amit, D. J., & Brunel, N. (1997). Dynamics of a recurrent network of spiking neurons before and following learning. Network: Computational Neural Systems, 8(4), 373-404.
4. Angelucci, A., & Bullier, J. (2003). Reaching beyond the classical receptive field of V1 Neurons: Horizontal or feedback axons? Journal of Physiology, 97(2-3), 141-154.
5. Angelucci, A., Levitt, J. B., Walton, E. J. S., Hupé, J.-M., Bullier, J., & Lund, J. S. (2002). Circuits for local and global signal integration in primary visual cortex. Journal of Neuroscience, 22(19), 8633-8646.
6. Barnes, T., & Mingolla, E. (2013).Aneural model of visual figure-ground segregation from kinetic occlusion. Neural Networks, 37, 141-164.
7. Bastos, A. M., Usrey W.M., Adams, R. A.,Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695-711.
8. Bayerl, P., & Neumann, H. (2004). Disambiguating visualmotion through contextual feedback modulation. Neural Computation, 16(10), 2041-2066.
9. Bayerl, P., & Neumann, H. (2006). A neural model of feature attention in motion perception. BioSystems, 89(1-3), 208-215.
10. Beck, C., & Neumann, H. (2011). Combining feature selection and integration: A neural model for MT motion selectivity. PLoS ONE, 6(7), e21254.
11. Beierlein, M., Gibson, J. R., & Connors, B. W. (2003). Two dynamically distinct inhibitory networks in layer 4 of the neocortex. Journal of Neurophysiology, 90(5), 2987-3000.
12. Berzhanskaya, J., Grossberg, S., & Mingolla, E. (2007). Laminar cortical dynamics of visual form and motion interactions during coherent object motion perception. Spatial Vision, 20(4), 337-395.
13. Bonin, V., Mante, V., & Carandini, M. (2005). The suppressive field of neurons in lateral geniculate nucleus. Journal of Neuroscience, 25(47), 10844-10856.
14. Bosking, W. H., Zhang, Y., Schofield, B., & Fitzpatrick, D. (1997). Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. Journal of Neuroscience, 17(6), 2112-2127.
15. Bouecke, J. D., Tlapale, E., Kornprobst, P., & Neumann, H. (2011). Neural mechanisms of motion detection, integration, and segregation: From biology to artificial image processing systems. EURASIP Journal on Advances in Signal Processing, 2011, 781561.
16. Brosch, T., & Neumann, H. (2012a). The Brain's sequential parallelism: Perceptual decision-making and early sensory responses. In Proceedings of the InternationConference on Neural Information Processing (pp. 41-50). New York: Springer-Verlag.
17. Brosch, T., & Neumann, H. (2012b). The combination of HMAX and HOGs in an attention guided framework for object localization. In Proceedings of the International Conference on Pattern Recognition Applications and Methods (vol. 2, pp. 281-288). Piscataway, NJ: IEEE.
18. Brosch, T., & Neumann, H. (2014). Interaction of feedforward and feedback streams in visual cortex in a firing-ratemodel of columnar computations. Neural Networks, 54, 11-16.
19. Bullier, J. (2001). Integrated model of visual processing. Brain Research Reviews, 36(2-3), 96-107.
20. Bullier, J., McCourt, M. E., & Henry, G. H. (1988). Physiological studies on the feedback connection to the striate cortex from cortical areas 18 and 19 of the cat. Experimental Brain Research, 70(1), 90-98.
21. Carandini, M., & Heeger,D. J. (1994). Summation and division by neurons in primate visual cortex. Science, 264(5163), 1333-1336.
22. Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13, 51-62.
23. Carandini, M., Heeger, D. J., & Movshon, J. A. (1997). Linearity and normalization in simple cells of the macaque primary visual cortex. Journal of Neuroscience, 17(21), 8621-8644.
24. Carandini, M., Heeger, D. J., & Movshon, J. A. (1999). Linearity and gain control in V1 simple cells. In P. S. Ulinski, E. G. Jones, and A. Peters (Eds.), Cerebral cortex (pp. 401-443). New York: Kluwer Academic/Plenum.
25. Cardin, V., Friston, K. J., & Zeki, S. (2011). Top-down modulations in the visual form pathway revealed with dynamic causal modeling. Cerebral Cortex, 21(3), 550-562.
26. Chen, K., Song, X.-M., & Li, C.-Y. (2012). Contrast-dependent variations in the excitatory classical receptive field and suppressive nonclassical receptive field of cat primary visual cortex. Cerebral Cortex, 23, 283-292.
27. Churchland, P. S., Koch, C., & Sejnowski, T. J. (1990). What is computational neuroscience? In E. L. Schwartz (Ed.), Computational neuroscience (pp. 46-55). Cambridge, MA: MIT Press.
28. Corchs, S., & Deco, G. (2002). Large-scale neural model for visual attention: Integration of experimental single-cell and fMRI data. Cerebral Cortex, 12(4), 339-348.
29. Crick, F., & Koch, C. (1998). Constraints on cortical and thalamic projections: The no-strong-loops hypothesis. Nature, 391, 245-250.
30. Curtu, R., & Ermentrout, B. (2004). Pattern formation in a network of excitatory and inhibitory cells with adaptation. SIAM J. Applied Dynamical Systems, 3(3), 191-231.
31. Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience, 12, 43-56.
32. Deco, G., & Rolls, E. T. (2005). Neurodynamics of biased competition and cooperation for attention: A model with spiking neurons. Journal of Neurophysiology, 94(1), 295-313.
33. Desimone, R. (1998). Visual attention mediated by biased competition in extrastriate visual cortex. Philos. Trans. R. Soc. Lond. B, 353(1373), 1245-1255.
34. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Reviews of Neuroscience, 18, 193-222.
35. Dragoi, V., & Sur, M. (2000). Dynamic properties of recurrent inhibition in primary visual cortex contrast and orientation dependence of contextual effects. Journal of Neurophysiology, 83(2), 1019-1030.
36. Eckhorn, R. (1999). Neural mechanisms of visual feature binding investigated with microelectrodes and models. Visual Cognition, 6(3), 231-265.
37. Eckhorn, R., Reitboeck, H. J., Arndt, M., & Dicke, P. (1990). Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neural Computation, 2(3), 293-307.
38. Ellias, S. A., & Grossberg, S. (1975). Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks. Biological Cybernetics, 20(2), 69-98.
39. Endres, D., Neumann, H., Kolesnik, M., & Giese, M. A. (2011). Hooligan detection the effects of saliency and expert knowledge. In Proceedings of the International Conference on Imaging for Crime Detection and Prevention (pp. 1-6). Piscataway, NJ: IEEE.
40. Ermentrout, G. B., & Kopell, N. (1998). Fine structure of neural spiking and synchronization in the presence of conduction delays. PNAS, 95(3), 1259-1264.
41. Felisberti, F., & Derrington, A. M. (1999). Long-range interactions modulate the contrast gain in the lateral geniculate nucleus of cats. Visual Neuroscience, 16(5), 943-956.
42. Field, D. J., Hayes, A., & Hess, R. F. (1993). Contour integration by the human visual system: Evidence for a local "association field." Vision Research, 33(2), 173-193.
43. Friedrichs, K. O. (1944). The identity of weak and strong extensions of differential operators. Transactions of the American Mathematical Society, 55, 132-151.
44. Geisler, W. S., Perry, J. S., Super, B. J., & Gallogly, D. P. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41(6), 711-724.
45. Gilbert, C. D., & Li, W. (2013). Top-down influences on visual processing. Nature Reviews Neuroscience, 14, 350-363.
46. Girard, P., & Bullier, J. (1989). Visual activity in area V2 during reversible inactivation of area 17 in the macaque monkey. Journal of Neurophysiology, 62(6), 1287-1302.
47. Gove, A., Grossberg, S., & Mingolla, E. (1995). Brightness perception, illusory contours, and corticogeniculate feedback. Visual Neuroscience, 12(6), 1027-1052.
48. Grossberg, S. (1973). Contour enhancement, short-term memory, and constancies in reverberating neural networks. Studies in Applied Mathematics, 52, 213-257.
49. Grossberg, S. (1980). How does a brain build a cognitive code? Psychological Review, 87(1), 1-51.
50. Grossberg, S. (1988). Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks, 1, 17-61.
51. Grossberg, S. (2013). Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks, 37, 1-47.
52. Grossberg, S., & Mingolla, E. (1985). Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations. Perception and Psychophysics, 38(2), 141-171.
53. Hamker, F. H. (2004). A dynamic model of how feature cues guide spatial attention. Vision Research, 44(5), 501-521.
54. Hansen, T., & Neumann, H. (2004).Neuralmechanisms for the robust representation of junctions. Neural Computation, 16(5), 1013-1037.
55. Hansen, T., & Neumann, H. (2008). A recurrent model of contour integration in primary visual cortex. Journal of Vision, 8(8), 1-25.
56. Harrison, L. M., Stephan, K. E., Rees, G., & Fristona, K. J. (2007). Extra-classical receptive field effects measured in striate cortex with fMRI. NeuroImage, 34(3), 1199-1208.
57. Heeger, D. J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9(2), 191-197.
58. Herz, A. V. M., Gollisch, T., Machens, C. K., & Jaeger, D. (2006). Modeling singleneuron dynamics and computations: A balance of detail and abstraction. Science, 314(5796), 80-85.
59. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500-544.
60. Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurones in the cat's striate cortex. Journal of Physiology, 148(3), 574-591.
61. Hubel, D. H., & Wiesel, T.N. (1972). Laminar and columnar distribution of geniculocortical fibers in the macaque monkey. Journal Comparative Neurology, 146, 421-450.
62. Hunter, J. N., & Born, R. T. (2011). Stimulus-dependent modulation of suppressive influences in MT. Journal of Neuroscience, 31(2), 678-686.
63. Hupé, J. M., James, A. C., Payne, B. R., Lomber, S. G., Girard, P., & Bullier, J. (1998). Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature, 394, 784-787.
64. Jadi, M. P., & Sejnowski, T. J. (2014a). Cortical oscillations arise from contextual interactions that regulate sparse coding. PNAS, 111(18), 6780-6785.
65. Jadi M. P., & Sejnowski, T. J. (2014b). Regulating cortical oscillations in an inhibitionstabilized network. Proceedings of the IEEE, 102(5), 830-842.
66. Jones, H. E., Grieve, K. L., Wang, W., & Sillito, A. M. (2001). Surround suppression in primate V1. Journal of Neurophysiology, 86(4), 2011-2028.
67. Jones, H. E.,Wang,W., & Sillito, A. M. (2002). Spatial organization and magnitude of orientation contrast interactions in primate V1. Journal of Neurophysiology, 88(5), 1796-1808.
68. Kapadia, M. K., Westheimer, G., & Gilbert, C. D. (2000). Spatial distribution of contextual interactions in primary visual cortex and in visual perception. Journal of Neurophysiology, 84(4), 2048-2062.
69. Kapadia, M. K., Ito, M., Gilbert, C. D., & Westheimer, G. (1995). Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in V1 of alert monkeys. Neuron, 15(4), 843-856.
70. Kiefer, M. (2007). Top-down modulation of unconscious "automatic" processes: A gating framework. Advances in Cognitive Psychology, 3(1-2), 289-306.
71. Knierim, J. J., & Van Essen, D. C. (1992). Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. Journal of Neurophysiology, 67(4), 961-980.
72. Koch, C. (1999). Biophysics of computation. New York: Oxford University Press.
73. Kouh, M., & Poggio, T. (2008). A canonical neural circuit for cortical nonlinear operations. Neural Computation, 20(6), 1427-1451.
74. Lamme,V. A. F. (1995). The neurophysiology visual cortex figure-ground segregation primary. Journal of Neuroscience, 15(2), 1605-1615.
75. Lamme, V. A. F., & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23(11), 571-579.
76. Larkum, M. (2013). A Cellular mechanism for cortical associations: An organizing principle for the cerebral cortex. Trends in Neurosciences, 36(3), 141-151.
77. Larkum,M. E., Senn,W., & Lüscher,H.-R. (2004). Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cerebral Cortex, 14(10), 1059-1070.
78. Latham, P. E., & Nirenberg, S. (2004). Computing and stability in cortical networks. Neural Computation, 16(7), 1385-1412.
79. Latham, P. E., Richmond, B. J., Nelson, P. G., & Nirenberg, S. (2000). Intrinsic dynamics in neuronal networks. I. Theory. Journal of Neurophysiology, 83(2), 808-827.
80. Latham, P. E., Richmond, B. J., Nirenberg, S., & Nelson, P. G. (2000). Intrinsic dynamics in neuronal networks. II: Experiment. Journal of Neurophysiology, 83(2), 828-835.
81. Layher, G., Giese, M., & Neumann, H. (2014). Learning representations of animated motion sequences: A neural model. Topics in Cognitive Science, 6(1), 170-182.
82. Lee, J., & Maunsell, J. H. R. (2009). A normalizationmodel of attentional modulation of single unit responses. PLoS ONE, 4(2), e4651.
83. Levick, W. R., Cleland, B. G., & Dubin, M. W. (1972). Lateral geniculate neurons of cat: Retinal inputs and physiology. Investigative Ophthalmology and Visual Science, 11(5), 302-311.
84. Li, X., & Basso, M. A. (2008). Preparing to move increases the sensitivity of superior colliculus neurons. Journal of Neuroscience, 28(17), 4561-4577.
85. Li, Z. (1999). Visual segmentation by contextual influences via intra-cortical interactions in the primary visual cortex. Network: Computation in Neural Systems, 10(2), 187-212.
86. Li, Z. (2001). Computational design and nonlinear dynamics of a recurrent network model of the primary visual cortex. Neural Computation, 13(8), 1749-1780.
87. Louie, K., & Glimcher, P.W. (2012). Efficient coding and the neural representation of value. Annals of the New York Academy of Sciences, 1251, 13-32.
88. Louie, K., Grattan, L. E., & Glimcher, P.W. (2011). Reward value-based gain control: Divisive normalization in parietal cortex. Journal of Neuroscience, 31(29), 10627-10639.
89. Lowe, D. G., & Binford, T. O. (1983). Perceptual organization as a basis for visual recognition. In Proceedings of the Third National Conference on Artificial Intelligence. (pp. 255-260). Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
90. Lyu, S., & Simoncelli, E. P. (2009). Nonlinear extraction of independent components of natural images using radial gaussianization. Neural Computation, 21(6), 1485-1519.
91. Markov, N. T., & Kennedy, H. (2013). The Importance of being hierarchical. Current Opinion in Neurobiology, 23(2), 187-194.
92. Martínez-Trujillo, J. C., & Treue, S. (2002).Attentional modulation strength in cortical area MT depends on stimulus contrast. Neuron, 35(2), 365-370.
93. Martinez-Trujillo, J. C., & Treue, S. (2004). Feature-based attention increases the selectivity of population responses in primate visual cortex. Current Biology, 14(9), 744-751.
94. McAdams, C. J., & Maunsell, J. H. R. (1999). Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. Journal of Neuroscience, 19(1), 431-441.
95. McCormick, D. A., Connors, B. W., Lighthall, J. W., & Prince, D. A. (1985). Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. Journal of Neurophysiology, 54(4), 782-806.
96. McLaughlin, D., Shapley, R., Shelley, M., & Wielaard, D. J. (2000). A neuronal network model of macaque primary visual cortex (V1), Orientation selectivity and dynamics in the input layer 4Ca. PNAS, 97(14), 8087-8092.
97. Moran, J., & Desimone, R. (1985). Selective attention gates visual processing in the extrastriate cortex. Science, 229(4715), 782-784.
98. Murphy, B. K., & Miller, K. D. (2009). Balanced amplification: A new mechanism of selective amplification of neural activity patterns. Neuron, 61(4), 635-648.
99. Mutch, J., & Lowe, D. G. (2008). Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision, 80(1), 45-57.
100. Naito, T., Sadakane, O., Okamoto, M., & Sato, H. (2007). Orientation tuning of surround suppression in lateral geniculate nucleus and primary visual cortex of cat. Neuroscience, 149(4), 962-975.
101. Naka, K. I., & Rushton, W. A. H. (1966). S-potentials from luminosity units in the retina of fish (Cyprinidae). Journal of Physiology, 185(3), 587-599.
102. Nassi, J. J., Gómez-Laberge, C., Kreiman, G., & Born, R. T. (2014). Corticocortical feedback increases the spatial extent of normalization. Frontiers in Systems Neuroscience, 8(105), 1-13.
103. Neumann, H., & Mingolla, E. (2001). Computational neural models of spatial integration in perceptual grouping. In T. Shipley & P. Kellman (Eds.), From fragments to objects: Grouping and segmentation in vision (pp. 353-400). Amsterdam: Elsevier.
104. Neumann, H., & Sepp, W. (1999). Recurrent V1-V2 interaction in early visual boundary processing. Biological Cybernetics, 81, 425-444.
105. Ohshiro, T., Angelaki, D. E., & DeAngelis, G. C. (2011). A normalization model of multisensory integration. Nature Neuroscience, 14(6), 775-782.
106. Ozeki, H., Finn, I. M., Schaffer, E. S.,Miller, K. D., & Ferster, D. (2009). Inhibitory stabilization of the cortical network underlies visual surround suppression. Neuron, 62(4), 578-592.
107. Piëch, V., Li, W., Reeke, G. N., & Gilbert, C. D. (2013). Network model of topdown influences on local gain and contextual interactions in visual cortex. PNAS, 110(43), E410817.
108. Polat, U., & Sagi, D. (1993). Lateral interactions between spatial channels: Suppression and facilitation revealed by lateral masking experiments. Vision Research, 33(7), 993-999.
109. Przybyszewski, A. W., Gaska, J. P., Foote, W., & Pollen, D. A. (2000). Striate cortex increases contrast gain of macaque LGN neurons. Visual Neuroscience, 17(4), 485-494.
110. Rabinowitz, N. C.,Willmore, B.D. B., Schnupp, J.W. H., & King, A. J. (2011). Contrast gain control in auditory cortex. Neuron, 70(6), 1178-1191.
111. Rankin, J., Tlapale, E., Veltz, R., Faugeras, O., & Kornprobst, P. (2013). Bifurcation analysis applied to a model of motion integration with a multistable stimulus. Journal of Computational Neuroscience, 34(1), 103-124.
112. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79-87.
113. Raudies, F., Mingolla, E., & Neumann, H. (2011). A model of motion transparency processing with local center-surround interactions and feedback. Neural Computation, 23, 2868-2914.
114. Rauss, K., Schwartz, S., & Pourtoise, G. (2011). Top-down effects on early visual processing in humans:Apredictive coding framework. Neuroscience and Biobehavioral Reviews, 35(5), 1237-1253.
115. Recanzone, G. H., & Wurtz, R. H. (2000). Effects of attention ON MT and MST neuronal activity during pursuit initiation. Journal of Neurophysiology, 83(2), 777-790.
116. Reynolds, J. H., Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19(5), 1736-1753.
117. Reynolds, J. H., & Desimone, R. (2003). Interacting roles of visual attention and visual saliency in V4. Neuron, 37(5), 853-863.
118. Reynolds, J. H., & Heeger, D. J. (2009). The normalizationmodel of attention. Neuron, 61, 168-185.
119. Reynolds, J. H., Pasternak, T., & Desimone, R. (2000). Attention increases sensitivity of V4 neurons. Neuron, 26(3), 703-714.
120. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 1019-1025.
121. Riesenhuber, M., & Poggio, T. (2000). Models of object recognition. Nature Neuroscience, 3, 1199-1204.
122. Roelfsema, P. R., Lamme, V. A. F., Spekreijse, H., & Bosch, H. (2002). Figure-ground segregation in a recurrent network architecture. Journal of Cognitive Neuroscience, 14(4), 525-537.
123. Salin, P.-A., & Bullier, J. (1995). Corticocortical connections in the visual system: Structure and function. Physiological Reviews, 75(1), 107-154.
124. Salinas, E. (2003). Background synaptic activity as a switch between dynamical states in a network. Neural Computation, 15(7), 1439-1475.
125. Sandell, J. H., & Schiller, P. H. (1982). Effect of cooling area 18 on striate cortex cells in the squirrel monkey. Journal of Neurophysiology, 48(1), 38-48.
126. Schwabe, L., Obermayer, K., Angelucci, A., & Bressloff, P. C. (2006). The role of feedback in shaping the extra-classical receptive field of cortical neurons: A recurrent network model. Journal of Neuroscience, 26(36), 9117-9129.
127. Self, M. W., Kooijmans, R. N., Supèr, H., Lamme, V. A., & Roelfsema, P. R. (2012). Different glutamate receptors convey feedforward and recurrent processing in macaque V1. PNAS, 109(27), 11031-11036.
128. Seriès, P., & Tarroux, P. (1999). Synchrony and delay activity in cortical column models. Neurocomputing, 26-27, 505-510.
129. Serre, T., Wolf, L., & Poggio, T. (2005). Object recognition with features inspired by visual cortex. In Proceedings of the Conference on Computer Vision and Pattern Recognition (pp. 994-1000). Piscataway, NJ: IEEE.
130. Shapley, R.M., & Xing, D. (2013). Local circuit inhibition in the cerebral cortex as the source of gain control and untuned suppression. Neural Networks, 37, 172-181.
131. Sherman, S. M., & Guillery, R. W. (1998). On the actions that one nerve cell can have on another: Distinguishing "drivers" from "modulators." PNAS, 95(12), 7121-7126.
132. Sillito, A. M., Cudeiro, J., & Jones, H. E. (2006). Always returning: Feedback and sensory processing in visual cortex and thalamus. Trends in Neurosciences, 29(6), 307-316.
133. Smith, M. A. (2006). Surround suppression in the early visual system. Journal of Neuroscience, 26(14), 3624-3625.
134. Somers, D. C., Todorov, E. V., Siapas, A. G., Toth, L. J., Kim, D., & Sur, M. (1998). A local circuit approach to understanding integration of long-range inputs in primary visual cortex. Cerebral Cortex, 8(3), 204-217.
135. Sperling, G. (1970).Model of visual adaptation and contrast detection. Perception and Psychophysics, 8(3), 143-157.
136. Spitzer, H., Desimone, R., & Moran, J. (1988). Increased attention enhances both behavioral and neuronal performance. Science, 240(4850), 338-340.
137. Spratling, M.W. (2008). Reconciling predictive coding and biased competition models of cortical function. Frontiers in Computational Neuroscience, 2(4), 1-8.
138. Spratling, M.W., & Johnson, M.H. (2004). Neural coding strategies and mechanisms of competition. Cognitive Systems Research, 5, 93-117.
139. Stemmler, M., Usher, M., & Niebur, E. (1995). Lateral interactions in primary visual cortex:Amodel bridging physiology and psychophysics. Science, 269(5232), 1877-1880.
140. Stimberg, M., Wimmer, K., Martin, R., Schwabe, L., Mariño, J., Schummers, J., Obermayer, K. (2009). The operating regime of local computations in primary visual cortex. Cerebral Cortex, 19(9), 2166-2180.
141. Tao, L., Shelley, M., McLaughlin, D., & Shapley, R. (2004). An egalitarian network model for the emergence of simple and complex cells in visual cortex. PNAS, 101(1), 366-371.
142. Terman, D., Kopell, N., & Bose, A. (1998). Dynamics of two mutually coupled slow inhibitory neurons. Physica D, 117(1-4), 241-275.
143. Thielscher, A., & Neumann, H. (2003). Neural mechanisms of cortico-cortical interaction in texture boundary detection: A modeling approach. Neuroscience, 122, 921-939.
144. Tononi, G., Sporns, O., & Edelman, G. M. (1992). Reentry and the problem of integrating multiple cortical areas: Simulation of dynamic integration in the visual system. Cerebral Cortex, 2(4), 310-335.
145. Treue, S. (2001). Neural correlates of attention in primate visual cortex. Trends in Neurosciences, 24(5), 295-300.
146. Treue, S., & Martìnez-Trujillo, J. C. (1999). Feature-based attention influences motion processing gain in macaque visual cortex. Nature, 399, 575-579.
147. Tsodyks, M., & Gilbert, C. (2004). Neural networks and perceptual learning. Nature, 431, 775-781.
148. Tsodyks, M. V., Skaggs, W. E., Sejnowski, T. J., & McNaughton, B. L. (1997). Paradoxical effects of external modulation of inhibitory interneurons. Journal of Neuroscience, 17(11), 4382-4388.
149. Tsui, J.M. G., Hunter, J. N., Born, R. T., & Pack, C. C. (2010). The role of V1 surround suppression in MT motion integration. Journal of Neurophysiology, 103, 3123-3138.
150. Ullman, S. (1995). Sequence seeking and counter streams: A computational model for bidirectional information flow in the visual cortex. Cerebral Cortex, 5(1), 1-11.
151. van Vreeswijk, C., Abbott, L. F., & Ermentrout, G. B. (1994). When inhibition not excitation synchronizes neural firing. Journal of Computational Neuroscience, 1(4), 313-321.
152. van Vreeswijk, C., & Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274(5293), 1724-1726.
153. Veltz, R., & Faugeras, O. (2010). Local global analysis of the stationary solutions of some neural field equations. SIAM J. of Applied Dynamical Systems, 9(3), 954-998.
154. Vogels, T. P., & Abbott, L. F. (2009). Gatingmultiple signals through detailed balance of excitation and inhibition in spiking networks. Nature Neuroscience, 12(4), 483-491.
155. Weidenbacher, U., & Neumann, H. (2009). Extraction of surface-related features in a recurrent model of V1-V2 interactions. PloS ONE, 4(6), e5909.
156. Williford, T., & Maunsell, J. H. R. (2006). Effects of spatial attention on contrast response functions in macaque area V4. Journal of Neurophysiology, 96(1), 40-54.
157. Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12(1), 1-24.
158. Wilson, H. R., & Cowan, J. D. (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13(2), 55-80.
159. Yu, A. J., Giese, M. A., & Poggio, T. A. (2002). Biophysiologically plausible implementations of the maximum operation. Neural Computation, 14(12), 2857-2881.
160. Yu, C., & Levi, D. M. (2000). Surround modulation in human vision unmasked by masking experiments. Nature Neuroscience, 3(7), 724-728.
161. Zhaoping, L. (1998). A neural model of contour integration in the primary visual cortex. Neural Computation, 10(4), 903-940.
162. Zhaoping, L. (2011). Neural circuit models for computations in early visual cortex. Current Opinion in Neurobiology, 21(5), 808-815.
163. Zhu, W., Shelley, M., & Shapley, R. (2009). A neuronal network model of primary visual cortex explains spatial frequency selectivity. Computational Neuroscience, 26(2), 271-287.