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The response dynamics of primate visual cortical neurons to simulated optical blur

Published online by Cambridge University Press:  26 August 2009

MICHAEL L. RISNER  and
TIMOTHY J. GAWNE
MICHAEL L. RISNER*
Affiliation:
Department of Vision Sciences, University of Alabama at Birmingham (UAB), Birmingham, Alabama
TIMOTHY J. GAWNE
Affiliation:
Department of Vision Sciences, University of Alabama at Birmingham (UAB), Birmingham, Alabama
*
*Address correspondence and reprint requests to: Michael L. Risner, Department of Vision Sciences, University of Alabama at Birmingham, 924 South 18th Street, Birmingham, AL 35294. E-mail: [email protected]
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Abstract

Neurons in visual cortical area V1 typically respond well to lines or edges of specific orientations. There have been many studies investigating how the responses of these neurons to an oriented edge are affected by changes in luminance contrast. However, in natural images, edges vary not only in contrast but also in the degree of blur, both because of changes in focus and also because shadows are not sharp. The effect of blur on the response dynamics of visual cortical neurons has not been explored. We presented luminance-defined single edges in the receptive fields of parafoveal (1–6 deg eccentric) V1 neurons of two macaque monkeys trained to fixate a spot of light. We varied the width of the blurred region of the edge stimuli up to 0.36 deg of visual angle. Even though the neurons responded robustly to stimuli that only contained high spatial frequencies and 0.36 deg is much larger than the limits of acuity at this eccentricity, changing the degree of blur had minimal effect on the responses of these neurons to the edge. Primates need to measure blur at the fovea to evaluate image quality and control accommodation, but this might only involve a specialist subpopulation of neurons. If visual cortical neurons in general responded differently to sharp and blurred stimuli, then this could provide a cue for form perception, for example, by helping to disambiguate the luminance edges created by real objects from those created by shadows. On the other hand, it might be important to avoid the distraction of changing blur as objects move in and out of the plane of fixation. Our results support the latter hypothesis: the responses of parafoveal V1 neurons are largely unaffected by changes in blur over a wide range.

Type
Research Articles
Information
Visual Neuroscience , Volume 26 , Issue 4 , July 2009 , pp. 411 - 420
Copyright
Copyright © Cambridge University Press 2009

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References

Abeles, M. & Goldstein, M.H. Jr. (1977). Multispike train analysis. Proceedings of the IEEE 65, 762–773.CrossRefGoogle Scholar
Albrecht, D.G. (1995). Visual cortex neurons in monkey and cat: Effect of contrast on spatial and temporal phase transfer function. Visual Neuroscience 12, 1191–1210.CrossRefGoogle Scholar
Allen, E.A. & Freeman, R.D. (2006). Dynamic spatial processing originate early in the visual pathways. Journal of Neurophysiology 26, 11763–11774.Google ScholarPubMed
Anderson, C.H. & Van Essen, D.C. (1987). Shifter circuits: A computational strategy for dynamic aspects of visual processing. Proceedings of the National Academy of Sciences 84, 6297–6301.CrossRefGoogle ScholarPubMed
Bobak, P., Bodis-Wollner, I. & Guillory, S. (1987). The effect of blur and contrast on VEP latency: Comparison between check and sinusoidal grating patterns. Electroencephalography and Clinical Neurophysiology 68, 247–255.CrossRefGoogle Scholar
Bonds, A.B. (1991). Temporal dynamics of contrast gain in single cells of the cat striate cortex. Visual Neuroscience 6, 239–255.CrossRefGoogle ScholarPubMed
Bredfeldt, C.E. & Ringach, D.L. (2002). Dynamics of spatial frequency tuning in macaque V1. The Journal of Neuroscience 22, 1976–1984.CrossRefGoogle ScholarPubMed
Carandini, M., Heeger, D.J. & Movshon, J.A. (1997). Linearity and normalization in simple cells in the macaque primary visual cortex. The Journal of Neuroscience 17, 8621–8644.CrossRefGoogle ScholarPubMed
Cook, R.L., Porter, T. & Carpenter, L. (1984). Distributed ray tracing. Computer Graphics 19, 137–145.CrossRefGoogle Scholar
Frazor, R.A., Albrecht, D.G., Geisler, W.S. & Crane, A.M. (2004). Visual cortex neurons of monkey and cats: Temporal dynamics of the spatial frequency response function. Journal of Neurophysiology 91, 2607–2627.CrossRefGoogle ScholarPubMed
Gamlin, P.D.R., McDougal, D.H., Pokorny, J., Smith, V.C., Yau, K.-W. & Dacey, D.M. (2007). Human and macaque pupil responses driven by melanopsin-containing retinal ganglion cells. Vision Research 47, 946–954.CrossRefGoogle ScholarPubMed
Gatass, R., Gross, C.E. & Sandell, J.H. (1981). Visual topography of V2 in the macaque. The Journal of Comparative Neurology 201, 519–539.CrossRefGoogle Scholar
Gawne, T.J. (1999 a). Temporal coding as a means of information transfer in the primate visual system. Critical Review in Neurobiology 13, 83–101.CrossRefGoogle ScholarPubMed
Gawne, T.J. (1999 b). Evidence against cross-orientation inhibition in the responses of macaque V1 cortical complex cells. Society for Neuroscience Abstract 25.Google Scholar
Gawne, T.J. (2000). The simultaneous coding of orientation and contrast in the response of V1 complex cells. Experimental Brain Research 133, 293–302.CrossRefGoogle ScholarPubMed
Gawne, T.J., Kjaer, T.W. & Richmond, B.J. (1996). Latency: Another potential code for feature binding in striate cortex. Journal of Neurophysiology 76, 1356–1360.CrossRefGoogle ScholarPubMed
Gawne, T.J. & Martin, J.M. (2002). Responses of primate visual cortical neurons to stimuli presented by flash, saccade, blink, and external darkening. Journal of Neurophysiology 88, 2178–2186.CrossRefGoogle ScholarPubMed
Kiorpes, L. & Kiper, D.C. (1992). Detection of Vernier offsets in the central and peripheral visual field in macaque monkeys. Investigative Ophthalmology & Visual Science 33(Suppl.), 1256.Google Scholar
Kiorpes, L. & Kiper, D.C. (1996). Development of contrast sensitivity across the visual field in macaque monkeys (Macaca nemestrina). Vision Research 36, 239–246.CrossRefGoogle ScholarPubMed
Lapuerta, P. & Schein, S.J. (1995). A four-surface schematic eye of macaque monkey obtained by an optical method. Vision Research 35, 2245–2254.CrossRefGoogle ScholarPubMed
Lee, J., Williford, T. & Maunsell, J.H.R. (2007). Spatial attention and the latency of the neuronal responses in macaque area V4. The Journal of Neuroscience 27, 9632–9637.CrossRefGoogle ScholarPubMed
Leigh, J.R. & Zee, D.S. (2006). The Neurology of Eye Movements (4th ed.). New York: Oxford University Press.Google Scholar
Marr, D. & Poggio, T. (1979). A computational theory of human stereo vision. Proceedings of the Royal Society of London 204, 301–328.Google ScholarPubMed
Mazer, J.A., Vinje, W.E., McDermott, J., Schiller, P.H. & Gallant, J.L. (2002). Spatial frequency and orientation tuning dynamics in area V1. Proceedings of the National Academy of Sciences 99, 1645–1650.CrossRefGoogle ScholarPubMed
Menz, M.D. & Freeman, R.D. (2003). Stereoscopic depth processing in the visual cortex: Coarse-to-fine mechanism. Nature Neuroscience 6, 59–65.CrossRefGoogle ScholarPubMed
Nishihara, H.K. (1984). Practical real-time imaging stereo matcher. Optical Engineering 23, 536–545.CrossRefGoogle Scholar
Ohzawa, I., Sclar, G. & Freeman, R.D. (1985). Contrast gain control in the cat’s visual system. Journal of Neurophysiology 54, 651–667.CrossRefGoogle ScholarPubMed
Oram, M.W., Xiao, D., Dritschel, B. & Payne, K.R. (2002). The temporal resolution of neural codes: Does response latency have a unique role? Philosophical Transactions of the Royal Society of London. Series B 357, 987–1001.CrossRefGoogle ScholarPubMed
Parker, D.M., Lishman, J.R. & Hughes, J. (1997). Evidence for the view that temporospatial integration in vision is temporally anisotropic. Perception 26, 1169–1180.CrossRefGoogle ScholarPubMed
Reid, R.C., Victor, J.D. & Shapley, R.M. (1992). Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex. Visual Neuroscience 9, 39–45.CrossRefGoogle ScholarPubMed
Richmond, B.J. & Optican, L.M. (1990). Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. II. Information transmission. Journal of Neurophysiology 57, 132–146.CrossRefGoogle Scholar
Risner, M.L. & Gawne, T.J. (2007). The response dynamics of visual cortical neurons to simulated blur. Society for Neuroscience Abstract 279.12.Google Scholar
Sclar, G. & Freeman, R.D. (1982). Orientation selectivity in the cat’s striate cortex is invariant with stimulus contrast. Experimental Brain Research 46, 457–461.CrossRefGoogle ScholarPubMed
Siguenza, J., Heide, W. & Creutzfeldt, O.D. (1987). Representation of edges of variable blur by neuronal responses in the lateral geniculate body and the visual cortex of cats: Limits of linear prediction. Vision Research 27, 1701–1717.CrossRefGoogle ScholarPubMed
Skavenski, A.A., Robinson, D.A., Steinman, R.M. & Timberlake, G.T. (1975). Miniature eye movements of fixation in rhesus monkey. Vision Research 15, 1269–1273.CrossRefGoogle ScholarPubMed
Sokol, S. & Moskowitz, A. (1981). Effect of retinal blur on the peak latency of the pattern evoked potential. Vision Research 21, 1279–1286.CrossRefGoogle ScholarPubMed