Theoretical understanding of the early visual processes by data compression and data selection

References

• Allman J, Miezin F, McGuinness E. Stimulus specific responses from beyond the classical receptive field: Neurophysiological mechanisms for local-global comparisons in visual neurons. Annu. Rev. Neurosci. 1985; 8: 407–430
   PubMed Web of Science ®Google Scholar
• Atick JJ, Redlich AN. Towards a theory of early visual processing. Neural Computation 1990; 2: 308–320
   Web of Science ®Google Scholar
• Atick JJ. Could information theory provide an ecological theory of sensory processing. Network: Computation and Neural Systems 1992; 3: 213–251
   Web of Science ®Google Scholar
• Atick JJ, Li Z, Redlich AN. Understanding retinal color coding from first principles. Neural Computation 1992; 4(4)559–572
   Web of Science ®Google Scholar
• Atick JJ, Li Z, Redlich AN. What does post-adaptation color appearance reveal about cortical color representation?. Vision Res. 1993; 33(1)123–129
   PubMed Web of Science ®Google Scholar
• Buchsbaum G, Gottschalk A. Trichromacy, opponent colours coding and optimum colour information transmission in the retina. Proc. R. Soc. Lond. B Biol. Sci. 1983; 220(1218)89–113
   PubMed Web of Science ®Google Scholar
• Barlow HB. Possible principles underlying the transformations of sensory messages. Sensory Communication, WA Rosenblith. MIT Press, Cambridge, MA 1961; 217–234
   Google Scholar
• Barlow HB. The Ferrier Lecture, 1980: Critical limiting factors in the design of the eye and visual cortex. Proc. R. Soc. London B 1981; 212: 1–34
   PubMed Web of Science ®Google Scholar
• Bell AJ, Sejnowski TJ. The ‘independent components’ of natural scenes are edge filters. Vision Res. 1997; 23: 3327–3338
   Google Scholar
• Bressloff PC, Cowan JD, Golubitsky M, Thomas PJ, Wiener MC. What geometric visual hallucinations tell us about the visual cortex. Neural Comput. 2002; 14(3)473–491
   PubMed Web of Science ®Google Scholar
• Casagrande VA, Guillery RW, Sherman SM. Cortical function: A view from the thalamus. Progress in Brain Research. Elsevier. 2005; 149, editors
   Google Scholar
• Dan Y, Atick JJ, Reid RC. Efficient coding of natural scenes in the lateral geniculate nucleus: Experimental test of a computational theory. J. Neurosci. 1996; 16(10)3351–3362
   PubMed Web of Science ®Google Scholar
• Daubechies I. The lectures on wavelets. SIAM. 1992
   Google Scholar
• Dong DW, Atick JJ. Temporal decorrelation: A theory of lagged and non-lagged responses in the lateral geniculate nucleus. Network: Computation in Neural Systems 1995; 6: 159–178
   Web of Science ®Google Scholar
• Duncan J, Humphreys GW. Visual search and stimulus similarity. Psychological Rev. 1989; 96(3)433–458
   PubMed Web of Science ®Google Scholar
• Field DJ. Relations between the statistics of natural images and the response properties of cortical cells. Journal of Optical Society of America, A 1987; 4(12)2379–2394
   PubMedGoogle Scholar
• Field DJ. What the statistics of natural images tell us about visual coding. SPIE, Human vision, visual processing, and digital display. 1989; 1077: 269–276
   Google Scholar
• Freeman WT, Adelson EH. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991; 13(9)891–906
   Web of Science ®Google Scholar
• Gilbert CD, Wiesel TN. Clustered intrinsic connections in cat visual cortex. J. Neurosci. 1983; 3(5)1116–1133
   PubMed Web of Science ®Google Scholar
• Gottlieb JP, Kusunoki M, Goldberg ME. The representation of visual salience in monkey parietal cortex. Nature 1998; 391(6666)481–484
   PubMed Web of Science ®Google Scholar
• Horwitz GD, Albright TD. Paucity of chromatic linear motion detectors in macaque V1. J. Vis. 2005; 5(6)525–533
   PubMed Web of Science ®Google Scholar
• Itti L, Baldi P. Bayesian surprise attracts human attention. Advances in Neural Information Processsing Systems. MIT Press, Cambridge, MA 2006; 19: 1–8, (NIPS2005)
   Google Scholar
• Itti L, Koch C. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res. 2000; 40(10–12)1489–1506
   PubMed Web of Science ®Google Scholar
• Jones HE, Grieve KL, Wang W, Sillito AM. Surround suppression in primate V1. J. Neurophysiol. 2001; 86(4)2011–2028
   PubMed Web of Science ®Google Scholar
• Jonides J. Voluntary versus automatic control over the mind's eye's movement. Attention and Performance IX, JB Long, AD Baddeley. Lawrence Erlbaum Associates Inc., Hillsdale, NJ 1981; 187–203
   Google Scholar
• Julesz B. Textons, the elements of texture perception, and their interactions. Nature 1981; 290(5802)91–97
   PubMed Web of Science ®Google Scholar
• Kadir T, Brady M. Saliency, scale, and image description. International J. of Computer Vision 2001; 45(2)83–105
   Web of Science ®Google Scholar
• Kapadia MK, Ito M, Gilbert CD, Westheimer G. Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in V1 of alert monkeys. Neuron 1995; 15(4)843–856
   PubMed Web of Science ®Google Scholar
• Kelly DH. Information capacity of a single retinal channel. IEEE Trans. Information Theory 1962; 8: 221–226
   Web of Science ®Google Scholar
• Knierim JJ, Van Essen DC. Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. J. Neurophysiol. 1992; 67(4)961–980
   PubMed Web of Science ®Google Scholar
• Koch C, Ullman S. Shifts in selective visual attention: Towards the underlying neural circuitry. Hum. Neurobiol. 1985; 4(4)219–227
   PubMedGoogle Scholar
• Lennie P. The cost of cortical computation. Curr. Biol. 2003; 13(6)493–497
   PubMed Web of Science ®Google Scholar
• Lewis A, Garcia R, Zhaoping L. The distribution of visual objects on the retina: Connecting eye movements and cone distributions. Journal of vision 2003; 3(11)893–905
   PubMed Web of Science ®Google Scholar
• Lewis AS, Zhaoping L. Saliency from natural scene statistics. 2005 Abstract Viewer/Itinerary Planner. Society for Neuroscience, Washington, DC 2005, Program No. 821.11., 2005. Online
   Google Scholar
• Lewis AS, Zhaoping L. Are cone sensitivities determined by natural color statistics?. Journal of Vision 2006; 6(3)285–302, http://www.journalofvision.org/6/3/8/
   PubMed Web of Science ®Google Scholar
• Levy WB, Baxter RA. Energy efficient neural codes. Neural Computation 1996; 8(3)531–543
   PubMed Web of Science ®Google Scholar
• Lee TS. Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Analysis and Machine Intelligence 1996; 18: 959–971
   Web of Science ®Google Scholar
• Lee TS. Computations in the early visual cortex. J. Physiology, Paris 2003; 97(2–3)121–139
   PubMed Web of Science ®Google Scholar
• Li Zhaoping. Different retinal ganglion cells have different functional goals. International Journal of Neural Systems 1992; 3(3)237–248
   Google Scholar
• Li Zhaoping, Atick JJ. Towards a theory of striate cortex. Neural Computation 1994a; 6: 127–146
   Web of Science ®Google Scholar
• Li Zhaoping, Atick JJ. Efficient stereo coding in the multiscale representation. Network: Computation in neural systems 1994b; 5: 1–18
   Google Scholar
• Li Zhaoping. Understanding ocular dominance development from binocular input statistics. The neurobiology of computation. Proceeding of Computational Neuroscience Conference 1994. 1995, J Bower. Kluwer Academic Publishers, 397–402
   Google Scholar
• Li Zhaoping. A theory of the visual motion coding in the primary visual cortex. Neural Computation 1996; 8(4)705–730
   PubMed Web of Science ®Google Scholar
• Li Z. Primary cortical dynamics for visual grouping. Theoretical aspects of neural computation, KM Wong, I King, D-Y Yeung. Springer-verlag, Singapore January, 1998a; 155, 1998
   Google Scholar
• Li Z. A neural model of contour integration in the primary visual cortex. Neural Comput. 1998b; 10(4)903–940
   PubMed Web of Science ®Google Scholar
• Li Z. Visual segmentation without classification: A proposed function for primary visual cortex. Perception. ECVP, OxfordEngland 1998c; 27: 45, 1998, supplement
   Google Scholar
• Li Z. Visual segmentation by contextual influences via intra-cortical interactions in primary visual cortex. Network: Computation and neural systems 1999a; 10(2)187–212
   PubMed Web of Science ®Google Scholar
• Li Z. Contextual influences in V1 as a basis for pop out and asymmetry in visual search. Proc. Natl. Acad. Sci. USA, 1999b; 96(18)10530–10535
   PubMed Web of Science ®Google Scholar
• Li Zhaoping, Dayan P. Computational differences between asymmetrical and symmetrical networks. Network: Computation in Neural Systems 1999; 10(1)59–77
   PubMed Web of Science ®Google Scholar
• Li Z. Pre-attentive segmentation in the primary visual cortex. Spatial Vision 2000; 13(1)25–50
   PubMedGoogle Scholar
• Li Z. Computational design and nonlinear dynamics of a recurrent network model of the primary visual cortex. Neural Computation 2001; 13(8)1749–1780
   PubMed Web of Science ®Google Scholar
• Li Zhaoping. A saliency map in primary visual cortex. Trends in Cognitive Sciences 2002; 6(1)9–16
   PubMed Web of Science ®Google Scholar
• Linsker R. Perceptual neural organization: Some approaches based on network models and information theory. Annu. Rev. Neurosci. 1990; 13: 257–281
   PubMed Web of Science ®Google Scholar
• Livingstone MS, Hubel DH. Anatomy and physiology of a color system in the primate visual cortex. J. Neurosci. 1984; 4(1)309–356
   PubMed Web of Science ®Google Scholar
• Laughlin SB. A simple coding procedure enhances a neuron's information capacity. Z. Naturforsch [C] 1981; 36: 910–912
   PubMed Web of Science ®Google Scholar
• Meister M, Berry MJ. The neural code of the retina. NEURON 1999; 22(3)435–450
   PubMed Web of Science ®Google Scholar
• Nadal JP, Parga N. Information processing by a perceptron in an unsupervised learning task. Network: Computation and Neural Systems 1993; 4(3)295–312
   Web of Science ®Google Scholar
• Nadal JP, Parga N. Nonlinear neurons in the low-noise limit: A factorial code maximizes information transfer. Network: Computation and Neural Systems 1994; 5: 565–581
   Web of Science ®Google Scholar
• Nakayama K, Mackeben M. Sustained and transient components of focal visual attention. Vision Res. 1989; 29(11)1631–1647
   PubMed Web of Science ®Google Scholar
• Nirenberg S, Carcieri SM, Jacobs AL, Latham PE. Retinal ganglion cells act largely as independent encoders. Nature 2001; 411: 698–701
   PubMed Web of Science ®Google Scholar
• Nothdurft HC, Gallant JL, Van Essen DC. Response modulation by texture surround in primate area V1: Correlates of “popout” under anesthesia. Vis. Neurosci. 1999; 16: 15–34
   PubMed Web of Science ®Google Scholar
• Olshausen BA, Field DJ. Sparse coding with an overcomplete basis set: A strategy employed by V1?. Vision Research 1997; 37: 3311–3325
   PubMed Web of Science ®Google Scholar
• Olshausen BA, Field DJ. How close are we to understanding V1?. Neural Computation 2005; 17: 1665–1699
   PubMed Web of Science ®Google Scholar
• Pashler H. Attention. Psychology Press Ltd, East SussexUK 1998, editor
   Google Scholar
• Petrov Y, Zhaoping L. Local correlations, information redundancy, and sufficient pixel depth in natural images. J. Opt. Soc. Am. A. Opt. Image Sci. Vis. 2003; 20(1)56–66
   PubMed Web of Science ®Google Scholar
• Puchalla JL, Schneidman E, Harris RA, Berry MJ. Redundancy in the population code of the retina. Neuron 2005; 46(3)493–504
   PubMed Web of Science ®Google Scholar
• Rao RPN, Ballard DH. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive field effects. Nature Neuroscience 1999; 2: 79–87
   PubMed Web of Science ®Google Scholar
• Rockland KS, Lund JS. Intrinsic laminar lattice connections in primate visual cortex. J. Comp. Neurol. 1983; 216(3)303–318
   PubMed Web of Science ®Google Scholar
• Rubenstein BS, Sagi D. Spatial variability as a limiting factor in texture-discrimination tasks: Implications for performance asymmetries. J. Opt. Soc. Am. A. 1990; 7(9)1632–1643
   PubMedGoogle Scholar
• Salinas E, Abbott LF. Do simple cells in primary visual cortex form a tight frame?. Neural Comput. 2000; 12(2)313–335
   PubMed Web of Science ®Google Scholar
• Schreiber W. The measurement of third order probability distributions of television signals. IEEE Trans. Information Theory 1956; 2(3)94–105
   Web of Science ®Google Scholar
• Schwartz O, Simoncelli EP. Natural signal statistics and sensory gain control. Nat Neurosci. 2001; 4(8)819–825
   PubMed Web of Science ®Google Scholar
• Sillito AM, Grieve KL, Jones HE, Cudeiro J, Davis J. Visual cortical mechanisms detecting focal orientation discontinuities. Nature 1995; 378: 492–496
   PubMed Web of Science ®Google Scholar
• Simons DJ, Chabris CF. Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception 1999; 28: 1059–1074
   PubMed Web of Science ®Google Scholar
• Simoncelli E, Olshausen B. Natural image statistics and neural representation. Annual Review of Neuroscience 2001; 24: 1193–1216
   PubMed Web of Science ®Google Scholar
• Srinivasan MV, Laughlin SB, Dubs A. Predictive coding: A fresh view of inhibition in the retina. Proc. R. Soc. Lond. B Biol. Sci. 1982; 216(1205)427–459
   PubMed Web of Science ®Google Scholar
• Sziklai G. Some studies in the speed of visual perception. IEEE Transactions on Information Theory 1956; 2(3)125–128
   Web of Science ®Google Scholar
• Tehovnik EJ, Slocum WM, Schiller PH. Saccadic eye movements evoked by microstimulation of striate cortex. Eur. J. Neurosci. 2003; 17(4)870–878
   PubMed Web of Science ®Google Scholar
• Treisman AM, Gelade G. A feature-integration theory of attention. Cognit. Psychol. 1980; 12(1)97–136
   PubMed Web of Science ®Google Scholar
• Treisman A, Gormican S. Feature analysis in early vision: Evidence from search asymmetries. Psychol. Rev. 1988; 95(1)15–48
   PubMed Web of Science ®Google Scholar
• van Hateren J. A theory of maximizing sensory information. Biol Cybern 1992; 68(1)23–29
   PubMed Web of Science ®Google Scholar
• van Hateren J, Ruderman DL. Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proc. Biol. Sciences. 1998; 265(1412)2315–2320
   PubMed Web of Science ®Google Scholar
• Wachtler T, Sejnowski TJ, Albright TD. Representation of color stimuli in awake macaque primary visual cortex. Neuron 2003; 37(4)681–691
   PubMed Web of Science ®Google Scholar
• Wolfe JM, Cave KR, Franzel SL. Guided search: An alternative to the feature integration model for visual search. J. Experimental Psychol. 1989; 15: 419–433
   Google Scholar
• Wolfe JM. Visual Search, a review. Attention, H Pashler. Psychology Press Ltd., Hove, East SussexUK 1998; 13–74
   Google Scholar
• Zhaoping L. The primary visual cortex creates a bottom-up saliency map. Neurobiology of Attention, L Itti, G Rees, JK Tsotsos. Elsevier. 2005; 570–575, Chapter 93
   Google Scholar
• Zhaoping L, Hubner M, Anzai A (2006) Efficient stereo coding in the primary visual cortex and its experimental tests based on optical imaging and single cell data. Presented at Annual Meeting of Computational Neuroscience. Edinburgh, 2006, summer
   Google Scholar
• Zhaoping L, May KA. Irrelevance of feature maps for bottom up visual saliency in segmentation and search tasks. 2004 Abstract Viewer/Itinerary Planner. Society for Neurocience, Washington D.C. 2004, Program No. 20.1., Online
   Google Scholar
• Zhaoping L, Snowden RJ. A theory of a saliency map in primary visual cortex (V1) tested by psychophysics of color-orientation interference in texture segmentation. Visual Cognition 2006; 14(4–8)911–933
   Web of Science ®Google Scholar
• Zhaoping L (2006) Overcomplete representation for fast attentional selection by bottom up saliency in the primary visual cortex. Presented at European Conference on Visual Perception. August, 2006
   Google Scholar
• Zigmond MJ, Bloom FE, Lndis SC, Roberts JL, Squire LR. Fundamental neuroscience. Academic Press, New York 1999
   Google Scholar