Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System

PLoS Computational Biology

Volumn 9, Issue 8, 2013, Pages

  Zhu, Mengchen ^a   Rozell, Christopher J ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); ELECTROPHYSIOLOGY; NEUROLOGY; TUNING; VISION;

CELL RESPONSE; CLASSICAL RECEPTIVE FIELD; ELECTROPHYSIOLOGY STUDY; FIELD-EFFECT; NON-CLASSICAL RECEPTIVE FIELDS; NON-LINEAR RESPONSE; RESPONSE PROPERTIES; SENSORY CODING; SIMPLE CELL; SPARSE CODING;

DYNAMICAL SYSTEMS;

ARTICLE; CONTROLLED STUDY; MATHEMATICAL PARAMETERS; NERVE CELL NETWORK; NONCLASSICAL RECEPTIVE FIELD; ORIENTATION; PHENOMENOLOGY; RECEPTIVE FIELD; SENSORY SYSTEM ELECTROPHYSIOLOGY; SPARSE CODING; STIMULUS RESPONSE; TASK PERFORMANCE; VISUAL STIMULATION;

EID: 84883386670     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003191     Document Type: Article

References (83)

1. Hartline H, (1938) The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. American Journal of Physiology 121: 400-415.
2. Hubel D, Wiesel T, (1962) Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of Physiology 160: 106-154.
3. Seriès P, Lorenceau J, Frégnac Y, (2003) The "silent" surround of V1 receptive fields: theory and experiments. Journal of physiology-Paris 97: 453-474.
4. Carandini M, Heeger D, Movshon J, (1999) Linearity and gain control in V1 simple cells. Cerebral Cortex 7: 401-444.
5. Vinje W, Gallant J, (2000) Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287: 1273-1276.
6. Vinje W, Gallant J, (2002) Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1. Journal of Neuroscience 22: 2904-2915.
7. Felsen G, Touryan J, Dan Y, (2005) Contextual modulation of orientation tuning contributes to efficient processing of natural stimuli. Network: Computation in Neural Systems 16: 139-149.
8. Haider B, Krause M, Duque A, Yu Y, Touryan J, et al. (2010) Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation. Neuron 65: 107-121.
9. Albright T, Stoner G, (2002) Contextual Influences on Visual Processing. Annual Review of Neuroscience 25: 339-379.
10. Priebe NJ, Ferster D, (2012) Mechanisms of neuronal computation in mammalian visual cortex. Neuron 75: 194-208.
11. Dobbins A, Zucker SW, Cynader MS, (1987) Endstopped neurons in the visual cortex as a substrate for calculating curvature. Nature 329: 438-441.
12. Field D, Hayes A (2004) Contour integration and the lateral connections of V1 neurons. In: Chalupa L, Werner J, editors, The Visual Neurosciences, Cambridge, MA: The MIT Press. pp. 1069-1079.
13. Lamme VAF (2004) Beyond the classical receptive field: contextual modulation of V1 responses. In: Chalupa L, Werner J, editors, The Visual Neurosciences, Cambridge, MA: The MIT Press. pp. 720-732.
14. Olshausen B, Field D, (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607-609.
15. Rehn M, Sommer F, (2007) A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. Journal of Computational Neuroscience 22: 135-146.
16. Olshausen B, Field D, (2004) Sparse coding of sensory inputs. Current Opinion in Neurobiology 14: 481-487.
17. Niven JE, Laughlin SB, (2008) Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology 211: 1792-1804.
18. Baum E, Moody J, Wilczek F, (1988) Internal representations for associative memory. Biological Cybernetics 59: 217-228.
19. Yap HL, Charles AS, Rozell CJ (2012) The restricted isometry property for echo state networks with applications to sequence memory capacity. In: Proceedings of IEEE Statistical Signal Processing Workshop. IEEE, pp. 580-583.
20. Wolfe J, Houweling A, Brecht M, (2010) Sparse and powerful cortical spikes. Current Opinion in Neurobiology 20: 306-312.
21. Sachdev RNS, Krause MR, Mazer JA, (2012) Surround suppression and sparse coding in visual and barrel cortices. Frontiers in Neural Circuits 6: 43 doi: 10.3389/fncir.2012.00043.
22. Simoncelli E, (2003) Vision and the statistics of the visual environment. Current opinion in Neurobiology 13: 144-149.
23. Spratling M, (2010) Predictive Coding as a Model of Response Properties in Cortical Area V1. Journal of Neuroscience 30: 3531-3543.
24. Rozell C, Johnson D, Baraniuk R, Olshausen B, (2008) Sparse coding via thresholding and local competition in neural circuits. Neural Computation 20: 2526-2563.
25. Perrinet L, (2010) Role of homeostasis in learning sparse representations. Neural computation 22: 1812-1836.
26. Zylberberg J, Murphy JT, DeWeese MR, (2011) A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. PLoS Comput Biol 7: e1002250.
27. Balavoine A, Romberg J, Rozell C, (2012) Convergence and rate analysis of neural networks for sparse approximation. IEEE Transactions on Neural Networks and Learning Systems 23: 1377-1389.
28. Charles AS, Garrigues P, Rozell CJ, (2012) A common network architecture efficiently implements a variety of sparsity-based inference problems. Neural Computation 24: 3317-3339.
29. Shapero S, Charles A, Rozell CJ, Hasler P, (2012) Low power sparse approximation on reconfigurable analog hardware. Emerging and Selected Topics in Circuits and Systems, IEEE Journal on 2: 530-541.
30. Kim S, Koh K, Lustig M, Boyd S, Gorinevsky D, (2007) An Interior-Point Method for Large-Scale ℓ-Regularized Least Squares. IEEE journal of selected topics in signal processing 1: 606-617.
31. Hirsch J, Martinez L, (2006) Circuits that build visual cortical receptive fields. Trends in neurosciences 29: 30-39.
32. Rose D, (1977) Responses of single units in cat visual cortex to moving bars of light as a function of bar length. The Journal of Physiology 271: 1-23.
33. Lee H, Battle A, Raina R, Ng A, (2007) Efficient sparse coding algorithms. Advances in neural information processing systems 19: 801-808.
34. Wang C, Bardy C, Huang JY, FitzGibbon T, Dreher B, (2009) Contrast dependence of center and surround integration in primary visual cortex of the cat. Journal of Vision 9: 20.1-15 doi: 10.1167/9.1.20.
35. Webb B, Dhruv N, Solomon S, Tailby C, Lennie P, (2005) Early and late mechanisms of surround suppression in striate cortex of macaque. Journal of Neuroscience 25: 11666-11675 Available: http://www.jneurosci.org/content/25/50/11666.full.
36. Walker G, Ohzawa I, Freeman R, (2000) Suppression outside the classical cortical receptive field. Visual neuroscience 17: 369-379.
37. Sceniak M, Ringach D, Hawken M, Shapley R, (1999) Contrast's effect on spatial summation by macaque V1 neurons. Nature Neuroscience 2: 733-739 Available: http://www.nature.com/neuro/journal/v2/n8/full/nn0899_733.html.
38. Jones H, Grieve K, Wang W, Sillito A, (2001) Surround suppression in primate V1. Journal of Neurophysiology 86: 2011-2028.
39. Olshausen B, Field D, (2005) How close are we to understanding V1? Neural Computation 17: 1665-1699.
40. Song X, Li C, (2008) Contrast-dependent and contrast-independent spatial summation of primary visual cortical neurons of the cat. Cerebral Cortex 18: 331-336.
41. Jones H, Wang W, Sillito A, (2002) Spatial organization and magnitude of orientation contrast interactions in primate V1. Journal of neurophysiology 88: 2796-2808.
42. Levitt J, Lund J, (1997) Contrast dependence of contextual effects in primate visual cortex. Nature 387: 73-76.
43. Cavanaugh J, Bair W, Movshon J, (2002) Selectivity and spatial distribution of signals from the receptive field surround in macaque V1 neurons. Journal of Neurophysiology 88: 2547-2556.
44. Somers D, Todorov E, Siapas A, Toth L, Kim D, et al. (1998) A local circuit approach to understanding integration of long-range inputs in primary visual cortex. Cerebral Cortex 8: 204-217.
45. Carandini M, (2007) Melting the iceberg: contrast invariance in visual cortex. Neuron 54: 11-13.
46. Skottun B, Bradley A, Sclar G, Ohzawa I, Freeman R, (1987) The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior. Journal of Neurophysiology 57: 773-786.
47. Alitto H, Usrey W, (2004) Influence of contrast on orientation and temporal frequency tuning in ferret primary visual cortex. Journal of neurophysiology 91: 2797-2808.
48. Bonds AB, (1989) Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex. Visual Neuroscience 2: 41-55.
49. Xu H, Jeong HY, Tremblay R, Rudy B, (2013) Neocortical somatostatin-expressing gabaergic interneurons disinhibit the thalamorecipient layer 4. Neuron 77: 155-167.
50. Freeman T, Durand S, Kiper D, Carandini M, (2002) Suppression without inhibition in visual cortex. Neuron 35: 759-771.
51. Priebe N, Ferster D, (2006) Mechanisms underlying cross-orientation suppression in cat visual cortex. Nature Neuroscience 9: 552-561 Available: http://www.nature.com/neuro/journal/v9/n4/full/nn1660.html.
52. Spratling MW, (2011) Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function. Neural Computation 24: 60-103.
53. Shi J, Wielaard J, Sajda P (2008) Analysis of a Gain Control Model of V1: Is the Goal Redundancy Reduction? In: Proceedings of International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 4991-4994.
54. Spratling M, (2011) A single functional model accounts for the distinct properties of suppression in cortical area V1. Vision Research 51: 563-576.
55. Spratling M, (2012) Predictive coding accounts for v1 response properties recorded using reverse correlation. Biological Cybernetics 106: 37-49.
56. Spratling M, (2012) Predictive coding as a model of the v1 saliency map hypothesis. Neural Networks 26: 7-28.
57. Rao R, Ballard D, (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2: 79-87.
58. Schwartz O, Simoncelli E, (2001) Natural signal statistics and sensory gain control. Nature neuroscience 4: 819-825.
59. Coen-Cagli R, Dayan P, Schwartz O, (2012) Cortical surround interactions and perceptual salience via natural scene statistics. PLoS Comput Biol 8: e1002405.
60. Karklin Y, Lewicki M, (2008) Emergence of complex cell properties by learning to generalize in natural scenes. Nature 457: 83-86.
61. Lochmann T, Ernst UA, Denève S, (2012) Perceptual inference predicts contextual modulations of sensory responses. The Journal of Neuroscience 32: 4179-4195.
62. Zhu M, Rozell CJ (2010) Sparse coding models demonstrate some non-classical receptive field effects. In: Nineteenth Annual Computational Neuroscience Meeting CNS*2010.
63. Koulakov A, Rinberg D, (2011) Sparse incomplete representations: A potential role of olfactory granule cells. Neuron 72: 124-136.
64. Berkes P, White B, Fiser J, (2009) No evidence for active sparsification in the visual cortex. Advances in Neural Information Processing Systems 22: 108-116.
65. Lamme VAF, Zipser K, Spekreijse H, (1998) Figure-ground activity in primary visual cortex is suppressed by anesthesia. Proceedings of the National Academy of Sciences 95: 3263-3268.
66. Finn I, Priebe N, Ferster D, (2007) The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex. Neuron 54: 137-152.
67. Angelucci A, Bressloff P, (2006) Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons. Progress in Brain Research 54: 93-120.
68. Carandini M, Heeger D, Senn W, (2002) A synaptic explanation of suppression in visual cortex. Journal of Neuroscience 22: 10053-10065.
69. Zhu M, Olshausen BA, Rozell CJ (2012) Biophysically accurate inhibitory interneuron properties in a sparse coding network. In: Computational and Systems Neuroscience (Cosyne) Meeting.
70. Bock DD, Lee WCA, Kerlin AM, Andermann ML, Hood G, et al. (2011) Network anatomy and in vivo physiology of visual cortical neurons. Nature 471: 177-182.
71. Fino E, Yuste R, (2011) Dense inhibitory connectivity in neocortex. Neuron 69: 1188-1203.
72. Egger V, Feldmeyer D, Sakmann B, (1999) Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex. Nature Neuroscience 2: 1098-1105.
73. Barlow H, Földiák P (1989) Adaptation and decorrelation in the cortex. In: Durbin R, Miall C, Mitchison G, editors, The computing neuron. Addison-Wesley Longman Publishing Co., Inc., pp. 54-72.
74. Sceniak MP, Hawken MJ, Shapley R, (2001) Visual spatial characterization of macaque v1 neurons. Journal of Neurophysiology 85: 1873-1887.
75. Liu YJ, Hashemi-Nezhad M, Lyon DC, (2011) Dynamics of extraclassical surround modulation in three types of v1 neurons. Journal of Neurophysiology 105: 1306-1317.
76. 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: 962-975.
77. Cavanaugh J, Bair W, Movshon J, (2002) Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons. Journal of Neurophysiology 88: 2530.
78. Lamme VAF, Rodriguez-Rodriguez V, Spekreijse H, (1999) Separate processing dynamics for texture elements, boundaries and surfaces in primary visual cortex of the macaque monkey. Cerebral Cortex 9: 406-413.
79. Stettler D, Das A, Bennett J, Gilbert C, (2002) Lateral connectivity and contextual interactions in macaque primary visual cortex. Neuron 36: 739-750.
80. Garrigues P, Olshausen B, (2008) Learning horizontal connections in a sparse coding model of natural images. Advances in Neural Information Processing Systems 20: 505-512.
81. Albrecht D, Hamilton D, (1982) Striate cortex of monkey and cat: contrast response function. Journal of Neurophysiology 48: 217-237.
82. Carandini M, Ferster D, (2000) Membrane potential and firing rate in cat primary visual cortex. Journal of Neuroscience 20: 470-484.
83. Dayan P, Abbott L (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems. MIT Press.