Ординатура / Офтальмология / Английские материалы / Progress in Brain Research Visual Perception, Part I Fundamentals of Vision Low and Mid-Level Processes in Perception_2006

Cleland, B.G. and Lee, B.B. (1985) A comparison of visual responses of cat lateral geniculate nucleus neurones with those of ganglion cells afferent to them. J. Physiol. (Lond.), 369: 249–268.

Coogan, T.A. and Burkhalter, A. (1990) Conserved patterns of cortico-cortical connections define areal hierarchy in rat visual cortex. Exp. Brain Res., 80: 49–53.

Crook, J.M., Kisvarday, Z.F. and Eysel, U.T. (1998) Evidence for a contribution of lateral inhibition to orientation tuning and direction selectivity in cat visual cortex: reversible inactivation of functionally characterized sites combined with neuroanatomical tracing techniques. Eur. J. Neurosci., 10: 2056–2075.

Dantzker, J.L. and Callaway, E.M. (2000) Laminar sources of synaptic input to cortical inhibitory interneurons and pyramidal neurons. Nat. Neurosci., 3: 701–707.

De Valois, R.L., Albrecht, D.G. and Thorell, L.G. (1982) Spatial frequency selectivity of cells in macaque visual cortex. Vision Res., 22: 545–559.

Dean, A.F. and Tolhurst, D.J. (1983) On the distinctness of simple and complex cells in the visual cortex of the cat. J. Physiol., 344: 305–325.

DeAngelis, G.C., Ohzawa, I. and Freeman, R.D. (1993a) Spatiotemporal organization of simple-cell receptive fields in the cat’s striate cortex I. General characteristics and postnatal development. J. Neurophysiol., 69: 1091–1117.

DeAngelis, G.C., Ohzawa, I. and Freeman, R.D. (1993b) Spatiotemporal organization of simple-cell receptive fields in the cat’s striate cortex II. Linearity of temporal and spatial summation. J. Neurophysiol., 69: 1118–1135.

DeAngelis, G.C., Ohzawa, I. and Freeman, R.D. (1995) Receptive field dynamics in central visual pathways. Trends Neurosci., 18: 451–458.

Debanne, D., Shultz, D.E. and Fregnac, Y. (1998) Activitydependent regulation of ‘on’ and ‘off’ responses in cat visual cortical receptive fields. J. Physiol., 508: 523–548.

Douglas, R.J., Koch, C., Mahowald, M., Martin, K.A. and Suarez, H.H. (1995) Recurrent excitation in neocortical circuits. Science, 269: 981–985.

Douglas, R.J. and Martin, K.A. (2004) Neuronal circuits of the neocortex. Ann. Rev. Neurosci., 27: 419–451.

Einhauser, W., Kayser, C., Koning, P. and Kording, K.P. (2002) Learning the invariance properties of complex cells from their responses to natural stimuli. Eur. J. Neurosci., 15: 475–486.

Emerson, R.C., Bergen, J.R. and Adelson, E.H. (1992) Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Res., 32: 203–218.

Enroth-Cugell, C. and Lennie, P. (1975) The control of retinal ganglion cell discharge by receptive field surrounds. J. Physiol., 247: 551–578.

Enroth-Cugell, C. and Robson, J.G. (1984) Functional characteristics and diversity of cat retinal ganglion cells. Basic characteristics and quantitative description. Invest. Ophthalmol. Vis. Sci., 25: 250–267.

Ferster, D. (1988) Spatially opponent excitation and inhibition in simple cells of the cat visual cortex. J. Neurosci., 8: 1172–1180.

89

Ferster, D., Chung, S. and Wheat, H. (1996) Orientation selectivity of thalamic input to simple cells of cat visual cortex. Nature, 380: 249–252.

Ferster, D. and Miller, K.D. (2000) Neural mechanisms of orientation selectivity in the visual cortex. Ann. Rev. Neurosci., 23: 441–471.

Fitzpatrick, D. (1996) The functional organization of local circuits in visual cortex: insights from the study of tree shrew striate cortex. Cereb. Cortex, 6: 329–341.

Fitzpatrick, D. (2000) Seeing beyond the receptive field in primary visual cortex. Curr. Opin. Neurobiol., 10: 438–442.

Fleet, D.J., Wagner, H. and Heeger, D.J. (1996) Neural encoding of binocular disparity: energy models, position shifts and phase shifts. Vision Res., 36: 1839–1857.

Gaska, J.P., Jacobson, L.D., Chen, H.W. and Pollen, D.A. (1994) Space–time spectra of complex cell filters in the macaque monkey: a comparison of results obtained with pseudowhite noise and grating stimuli. Visual Neurosci., 11: 805–821.

Geisler, W.S. and Albrecht, D.G. (1992) Cortical neurons: isolation of contrast gain control. Vision Res., 32: 1409–1410.

Gil, Z., Connors, B.W. and Amitai, Y. (1999) Efficacy of thalamocortical and intracortical synaptic connections: quanta, innervation, and reliability. Neuron, 23: 385–397.

Gilbert, C.D. (1977) Laminar differences in receptive field properties of cells in cat primary visual cortex. J. Physiol., 268: 391–421.

Gilbert, C.D. (1983) Microcircuitry of the visual cortex. Ann. Rev. Neurosci., 6: 217–247.

Gilbert, C.D. and Wiesel, T.N. (1979) Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature, 280: 120–125.

Gillespie, D.C., Lampl, I., Anderson, J.S. and Ferster, D. (2001) Dynamics of the orientation-tuned membrane potential response in cat primary visual cortex. Nat. Neurosci., 4: 1014–1019.

Hartline, H.K. (1938) The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. Am. J. Physiol., 121: 400–415.

Heggelund, P. (1981) Receptive field organization of complex cells in cat striate cortex. Exp. Brain Res., 42: 90–107.

Heggelund, P. (1986) Quantitative studies of enhancement and suppression zones in the receptive field of simple cells in cat striate cortex. J. Physiol., 373: 293–310.

Henry, G.H. (1977) Receptive field classes of cells in the striate cortex of the cat. Brain Res., 133: 1–28.

Hirsch, J.A. (2003) Synaptic physiology and receptive field structure in the early visual pathway of the cat. Cereb. Cortex, 13: 63–69.

Hirsch, J.A., Alonso, J.M., Reid, R.C. and Martinez, L.M. (1998a) Synaptic integration in striate cortical simple cells. J. Neurosci., 18: 9517–9528.

Hirsch, J.A., Gallagher, C.A., Alonso, J.M. and Martinez, L.M. (1998) Ascending projections of simple and complex cells in layer 6 of the cat striate cortex. J. Neurosci., 18: 8086–8094.

Hirsch, J.A. and Martinez, L.M. (2006) Circuits that build visual cortical receptive fields. Trends Neurosci., 29: 30–39, doi:10.1016/j.tins.2005.11.001.

90

Hirsch, J.A., Martinez, L.M., Alonso, J.M., Desai, K., Pillai, C. and Pierre, C. (2002) Synaptic physiology of the flow of information in the cat’s visual cortex in vivo. J. Physiol., 540: 335–350.

Hirsch, J.A., Martinez, L.M., Alonso, J.M., Pillai, C. and Pierre, C. (2000) Simple and complex inhibitory cells in layer 4 of cat visual cortex. Soc. Neurosci. Abstr., 26: 108.

Hirsch, J.A., Martinez, L.M., Pillai, C., Alonso, J.M., Wang, Q. and Sommers, F.T. (2003) Functionally distinct inhibitory neurons at the first stage of visual cortical processing. Nat. Neurosci., 6: 1300–1308.

Hoffmann, K.P. and Stone, J. (1971) Conduction velocity of afferents to cat visual cortex: a correlation with cortical receptive field properties. Brain Res., 32: 460–466.

Hubel, D.H. and Wiesel, T.N. (1959) Receptive fields of single neurones in the cat’s striate cortex. J. Physiol., 148: 574–591.

Hubel, D.H. and Wiesel, T.N. (1961) Integrative action in the cat’s lateral geniculate body. J. Physiol., 155: 385–398.

Hubel, D.H. and Wiesel, T.N. (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol., 160: 106–154.

Hubel, D.H. and Wiesel, T.N. (1968) Receptive fields and functional architecture of monkey striate cortex. J. Physiol. (Lond.), 195: 215–243.

Hyvarinen, A. and Hoyer, P.O. (2001) A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. Vision Res., 41: 2413–2423.

Jacob, M.S., Peterson, M.R., Wu. A. and Freeman, R.D. (2003) Laminar differences in response characteristics of cells in the primary visual cortex. Soc. Neurosci. Abstr., Program No. 910.913.

Jones, J.P. and Palmer, L.A. (1987) The two-dimensional spatial structure of simple receptive fields in cat striate cortex. J. Neurophysiol., 58: 1187–1211.

Kagan, I., Gur, M. and Snodderly, D.M. (2002) Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings. J. Neurophysiol., 88: 2557–2574.

Katz, L.C. (1987) Local circuitry of identified projection neurons in cat visual cortex brain slices. J. Neurosci., 7: 1223–1249.

Kisvarday, Z.F., Martin, K.A., Freund, T.F., Magloczky, Z., Whitteridge, D. and Somogyi, P. (1986) Synaptic targets of HRP-filled layer III pyramidal cells in the cat striate cortex. Exp. Brain Res., 64: 541–552.

Kuffler, S. (1953) Discharge patterns and functional organization of the mammalian retina. J. Neurophysiol., 16: 37–68.

Lampl, I., Anderson, J.S., Gillespie, D.C. and Ferster, D. (2001) Prediction of orientation selectivity from receptive field architecture in simple cells of cat visual cortex. Neuron, 30: 263–274.

Lauritzen, T.Z. and Miller, K.D. (2003) Different roles for simpleand complex-cell inhibiton in V1. J. Neurosci., 23: 10201–10213.

LeVay, S. and Gilbert, C.D. (1976) Laminar patterns of geniculocortical projection in the cat. Brain Res., 113: 1–19.

Levick, W.R., Cleland, B.G. and Dubin, M.W. (1972) Lateral geniculate neurons of cat: retinal inputs and physiology. Invest. Ophthalmol., 11: 302–311.

Lund, J.S., Henry, G.H., MacQueen, C.L. and Harvey, A.R. (1979) Anatomical organization of the primary visual cortex (area 17) of the cat. A comparison with area 17 of the macaque monkey. J. Comp. Neurol., 184: 599–618.

Marino, J., Schummers, J., Lyon, D.C., Schwabe, L., Beck, O., Wiesing, P., Obermayer, K. and Sur, M. (2005) Invariant computations in local cortical networks with balanced excitation and inhibition. Nat. Neurosci., 8: 194–201.

Martin, K.A. (2002) Microcircuits in visual cortex. Curr. Opin. Neurobiol., 12: 418–425.

Martin, K.A. and Whitteridge, D. (1984) Form, function and intracortical projections of spiny neurones in the striate visual cortex of the cat. J. Physiol., 353: 463–504.

Martinez, L.M. and Alonso, J.M. (2001) Construction of complex receptive fields in primary visual cortex. Neuron, 32: 515–525.

Martinez, L.M. and Alonso, J.M. (2003) Complex receptive fields in primary visual cortex. Neuroscientist, 8: 317–331.

Martinez, L.M., Alonso, J.M., Reid, R.C. and Hirsch, J.A. (2002) Laminar processing of stimulus orientation in cat visual cortex. J. Physiol., 540: 321–333.

Martinez, L.M., Wang, Q., Reid, R.C., Pillai, C., Alonso, J.M., Sommer, F.T. and Hirsch, J.A. (2005) Receptive field structure field varies with layer in the primary visual cortex. Nat. Neurosci., 8: 372–379.

Mata, M.L. and Ringach, D.L. (2005) Spatial Overlap of ‘on’ and ‘off’ subregions and its relation to response modulation ratio in macaque primary visual cortex. J. Neurophysiol., 93: 919–928.

McLean, J. and Palmer, L.A. (1994) Organization of simple cell responses in the three-dimensional (3-D) frequency domain. Visual Neurosci., 11: 295–306.

Mechler, F. and Ringach, D.L. (2002) On the classification of simple and complex cells. Vision Res., 42: 1017–1033.

Monier, C., Chavane, F., Baudot, P., Graham, L.J. and Fregnac, Y. (2003) Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. Neuron, 37: 663–680.

Mooser, F., Bosking, W.H. and Fitzpatrick, D. (2004) A morphological basis for orientation tuning in primary visual cortex. Nat. Neurosci., 8: 872–879.

Movshon, J.A., Thompson, I.D. and Tolhurst, D.J. (1978a) Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. J. Physiol., 283: 53–77.

Movshon, J.A., Thompson, I.D. and Tolhurst, D.J. (1978b) Receptive field organization of complex cells in the cat’s striate cortex. J. Physiol., 283: 79–99.

Movshon, J.A., Thompson, I.D. and Tolhurst, D.J. (1978c) Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat’s visual cortex. J. Physiol., 283: 101–120.

Murthy, A. and Humphrey, A.L. (1999) Inhibitory contributions to spatiotemporal receptive-field structure and direction selectivity in simple cells of cat area 17. J. Neurophysiol., 81: 1212–1224.

Nelson, S. (2002) Cortical microcircuits. Diverse or canonical? Neuron, 36: 19–27.

Nelson, S., Toth, L., Sheth, B. and Sur, M. (1994) Orientation selectivity of cortical neurons during intracellular blockade of inhibition. Science, 265: 774–777.

Ohzawa, I., DeAngelis, G.C. and Freeman, R.D. (1990) Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science, 249: 1037–1041.

Ohzawa, I., DeAngelis, G.C. and Freeman, R.D. (1997) Encoding of binocular disparity by complex cells in the cat’s visual cortex. J. Neurophysiol., 77: 2879–2909.

Ohzawa, I. and Freeman, R D. (1986) The binocular organization of complex cells in the cat’s visual cortex. J. Neurophysiol., 56: 243–259.

Ohzawa, I. and Freeman, R.D. (1997) Spatial pooling of subunits in complex cell receptive fields. Soc. Neurosci. Abstr., 23: 1669.

Ohzawa, I., Sclar, G. and Freeman, R.D. (1985) Contrast gain control in the cat’s visual system. J. Neurophysiol., 54: 651–667.

Okajima, K. and Imaoka, H. (2001) A complex cell-like receptive field obtained by information maximization. Neural Comput., 13: 547–562.

Olshausen, B.A. and Field, D.J. (1996) Emergence of simplecell receptive field properties by learning a sparse code for natural images. Nature, 381: 607–609.

Orban, G.A. (1884) Neuronal Operations in the Visual Cortex. Springer, Berlin.

Palmer, L.A. and Davis, T.L. (1981) Receptive-field structure in cat striate cortex. J. Neurophysiol., 46: 260–276.

Palmer, L.A. and Rosenquist, A.C. (1974) Visual receptive fields of single striate cortical units projecting to the superior colliculus in the cat. Brain Res., 67: 27–42.

Peters, A. and Payne, B.R. (1993) Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. Cereb. Cortex, 3: 69–78.

Pei, X., Vidyasagar, T.R., Volgushev, M. and Creutzfeldt, O.D. (1994) Receptive field analysis and orientation selectivity of postsynaptic potentials of simple cells in cat visual cortex. J. Neurosci., 14: 7130–7140.

Pollen, D.A., Gaska, J.P. and Jacobson, L.D. (1989) Physiological constrains on models of visual cortical function. In: Cotterill, R.M.J. (Ed.), Models of Brain Function. Cambridge University Press, Cambridge, NY, pp. 115–135.

Priebe, N.J. and Ferster, D. (2002) A new mechanism for neuronal gain control (or how the gain in brains has mainly been explained). Neuron, 35: 602–604.

Priebe, N.J., Mechler, F., Carandini, M. and Ferster, D. (2004) The contribution of spike threshold to the dichotomy of cortical simple and complex cells. Nat. Neurosci., 7: 1113–1122.

Qian, N. and Zhu, Y. (1997) Physiological computation of binocular disparity. Vision Res., 37: 1811–1827.

Reid, R.C. and Alonso, J.M. (1995) Specificity of monosynaptic connections from thalamus to visual cortex. Nature, 378: 281–284.

91

Ringach, D.L. (2004) Mapping receptive fields in primary visual cortex. J. Physiol. (Lond.), 558.3: 717–728.

Ringach, D.L., Hawken, M.J. and Shapley, R. (1997) Dynamics of orientation tuning in macaque primary visual cortex. Nature, 387: 281–284.

Ringach, D.L., Hawken, M.J. and Shapley, R. (2003) Dynamics of orientation tuning in macaque V1: the role of global and tuned supression. J. Neurophysiol., 90: 342–352.

Ringach, D.L., Shapley, R.M. and Hawken, M.J. (2002) Orientation selectivity in macaque V1: diversity and laminar dependence. J. Neurosci., 22: 5639–5651.

Rivadulla, C., Sharma, J. and Sur, M. (2001) Specific roles of NMDA and AMPA receptors in direction-selective and spatial phase-selective responses in visual cortex. J. Neurosci., 21: 1710–1719.

Sakai, K. and Tanaka, S. (2000) Spatial pooling in the secondorder spatial structure of cortical complex cells. Vision Res., 40: 855–871.

Saul, A.B. and Humphrey, A.L. (1990) Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. J. Neurophysiol., 64: 206–224.

Schiller, P.H., Finlay, B.L. and Volman, S.F. (1976) Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. J. Neurophysiol., 39: 1288–1319.

Sclar, G. and Freeman, R.D. (1982) Orientation selectivity in the cat’s striate cortex is invariant with stimulus contrast. Exp. Brain Res., 46: 457–461.

Shapley, R. and Hochstein, S. (1975) Visual spatial summation in two classes of geniculate cells. Nature, 256: 411–413.

Shapley, R. and Lennie, P. (1985) Spatial frequency analysis in the visual system. Ann. Rev. Neurosci., 8: 547–583.

Sillito, A.M. (1975) The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat. J. Physiol. (Lond.), 250: 305–329.

Simons, D.J. (1978) Response properties of vibrissa units in rat somatosensory neocortex. J. Neurophysiol., 50: 798–820.

Skottun, B.C., De Valois, R.L., Grosof, D.H., Movshon, J.A., Albrecht, D.G. and Bonds, A.B. (1991) Classifying simple and complex cells on the basis of response modulation. Vision Res., 31: 1079–1086.

Somers, D.C., Nelson, S.B. and Sur, M. (1995) An emergent model of orientation selectivity in cat visual cortical simple cells. J. Neurosci., 15: 5448–5465.

Somers, D.C., Todorov, .E.V., Siapas, A.G., Toth, L.J., Kim, D.S. and Sur, M. (1998) A local circuit approach to understanding integration of long-range inputs in primary visual cortex. Cereb. Cortex, 8: 204–217.

Sompolinsky, H. and Shapley, R. (1997) New perspectives on the mechanisms for orientation selectivity. Curr. Opin. Neurobiol., 7: 514–522.

Stratford, K.J., Tarczy-Hornoch, K., Martin, K.A., Bannister, N.J. and Jack, J.J. (1996) Excitatory synaptic inputs to spiny stellate cells in cat visual cortex. Nature, 382: 258–261.

Swadlow, H.A. and Gusev, A.G. (2002) Receptive-field construction in cortical inhibitory interneurons. Nat. Neurosci., 5: 403–404.

92

Szulborski, R.G. and Palmer, L.A. (1990) The two-dimensional spatial structure of nonlinear subunits in the receptive fields of complex cells. Vision Res., 30: 249–254.

Tanaka, K. (1983) Cross-correlation analysis of geniculostriate neuronal relationships in cats. J. Neurophysiol., 49: 1303–1318.

Tanaka, K. (1985) Organization of geniculate inputs to visual cortical cells in the cat. Vision Res., 25: 357–364.

Tao, L., Shelley, M., McLaughlin, D. and Shapley, R. (2004) An egalitarian network model for the emergence of simple and complex cells in visual cortex. Proc. Natl. Acad. Sci. USA, 101: 366–371.

Thomson, A.M. and Bannister, A.P. (2003) Interlaminar connections in the neocortex. Cereb. Cortex, 13: 5–14.

Thomson, A.M., West, D.C., Wang, Y. and Bannister, A.P. (2002) Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2–5 of adult rat and cat neocortex: triple intracellular recordings and biocytin labelling in vitro. Cereb. Cortex, 12: 936–953.

Touryan, J., Felsen, G. and Dan, Y. (2005) Spatial structure of complex cell receptive fields measured with natural images. Neuron, 45: 781–791.

Toyama, K., Kimura, M. and Tanaka, K. (1981) Organization of cat visual cortex as investigated by cross-correlation analysis. J. Neurophysiol., 46: 202–214.

Troyer, T.W., Krukowski, A.E. and Miller, K.D. (2002) LGN input to simple cells and contrast-invariant orientation tuning: an analysis. J. Neurophysiol., 87: 2741–2752.

Troyer, T.W., Krukowski, A.E., Priebe, N.J. and Miller, K.D. (1998) Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J. Neurosci., 18: 5908–5927.

Usrey, W.M., Reppas, J.B. and Reid, R.C. (1999) Specificity and strength of retinogeniculate connections. J. Neurophysiol., 82: 3527–3540.

Usrey, W.M., Sceniak, M.P. and Chapman, B. (2003) Receptive fields and response properties of neurons in layer 4 of ferret visual cortex. J. Neurophysiol., 89: 1003–1015.

Van Hooser, S.D., Heimel, J.A. and Nelson, S.B. (2003) Receptive field properties and laminar organization of lateral geniculate nucleus in the gray squirrel (Sciurus carolinensis). J. Neurophysiol., 90: 3398–3418.

Van Horn, S.C., Erisir, A. and Sherman, S.M. (2000) Relative distribution of synapses in the A-laminae of the lateral geniculate nucleus of the cat. J. Comp. Neurol., 416: 509–520.

Victor, J.D. and Shapley, R.M. (1979) Receptive field mechanisms of cat X and Y retinal ganglion cells. J. Gen. Physiol., 74: 275–298.

Volgushev, M., Pei, X., Vidyasagar, T.R. and Creutzfeldt, O.D. (1993) Excitation and inhibition in orientation selectivity of cat visual cortex neurons revealed by whole-cell recordings in vivo. Visual Neurosci., 10: 1151–1155.

Wielaard, D.J., Shelley, M., Mclaughlin, D. and Shapley, R. (2001) How simple cells are made in a nonlinear network model of the visual cortex. J. Neurosci., 21: 5203–5211.

Wiesel, T.N. (1959) Recording inhibition and excitation in the cat’s retinal ganglion cells with intracellular electrodes. Nature, 183: 264–265.

Wiesel, T.N. and Gilbert, C.D. (1983) The Sharpey–Schafer lecture. morphological basis of visual cortical function. Quart. J. Exp. Physiol., 68: 525–543.

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Introduction

The perception of a visual ‘‘figure’’ often relies upon the overall spatial arrangement of its local elements. The global attributes of a visual stimulus can affect the response of visual cortical neurons to the local attributes of the stimulus. For example, in early visual cortex, the response of neurons to stimuli within their receptive field (RF) center can be modulated by contextual stimuli outside their RF, in the RF surround. The long-term goal of our research is to identify the neural circuits that generate responses within and outside the RF of visual cortical neurons. The anatomical substrates and mechanisms involved are likely to be the cornerstone of contour integration, and figure-ground segregation. Thus, these studies ultimately will help our understanding of the neural substrates for higher visual cortical processing and perception.

Here we review anatomical and physiological data suggesting potential circuits involved in center– surround interactions in primate V1, and propose specific mechanisms by which these circuits could generate such interactions. We present a neural network model implementing these ideas, whose architecture is constrained to fit the anatomical and physiological data presented in this review. Finally, we discuss specific predictions of the model, and report our recent physiological data that support such predictions.

Spatial properties of the receptive field center and surround of V1 neurons in the primate

A basic feature of neurons at the early stages of visual cortical processing is that they respond to presentation of specific visual stimuli within a localized region of space, i.e. the neuron’s RF. Presentation of similar stimuli outside the RF typically does not evoke a response from the neuron, but can modulate (facilitate or suppress) the neuron’s response to stimulation of its RF center (Blakemore and Tobin, 1972; Nelson and Frost, 1978; Allman et al., 1985; Gilbert and Wiesel, 1990; Li et al., 2001).

The RF center of a V1 cell consists of a lowthreshold spiking region (the minimum response

field, mRF; or classical RF, cRF) that can be mapped using small drifting gratings of optimal parameters for the neuron and delimiting the area of visual space that elicits spikes (Hubel and Wiesel, 1962; Barlow et al., 1967). The mRF is surrounded by a higher threshold depolarizing field (the high-contrast summation RF, or hsRF) that facilitates the cell’s response when stimulated together with the mRF. The hsRF is typically measured by centering a high-contrast drifting grating of optimal parameters for the cell on the cell’s mRF, and systematically increasing its diameter (DeAngelis et al., 1994; Sceniak et al., 2001; Cavanaugh et al., 2002; Levitt and Lund, 2002). A typical V1 cell increases its response with increasing grating diameter, up to a peak, and it is then suppressed (‘‘surround suppression’’) for further increases in stimulus diameter (Fig. 1a). The size of the hsRF is the stimulus diameter at peak response (gray arrow in Fig. 1a). In macaques, the hsRF size of V1 neurons at 2–81 eccentricity averages 1170.1 (Fig. 1b), and is about 2.2-fold larger than the mean mRF size of the same cells (Angelucci et al., 2002b; Levitt and Lund, 2002). The summation RF (sRF) has been shown to be on average 2.3-fold larger when measured using low-contrast gratings (Sceniak et al., 1999). We refer to the summation RF measured using low-contrast gratings as the ‘‘low-contrast sRF’’, or lsRF (Fig. 1d).

Many studies have examined the influence of stimuli outside the RF on the response of V1 cells to stimuli inside their RF. A distinctive property of center–surround interactions is their orientation specificity. However, there is less agreement among studies on the specific ‘‘sign’’ (i.e. facilitation or suppression) of the orientation-specific interactions. We (Angelucci and Bullier, 2003) have previously argued that this discrepancy may be due to the different definitions of RF center and surround adopted in the different studies. Specifically, in some studies, the RF center size was defined as the neuron’s sRF (hsRF or lsRF), and the surround as the region outside it. In the reminder of this chapter, we will refer to this region outside the lsRF as the ‘‘far’’ surround (Fig. 2a, left). In these studies, large gratings of similar orientation to the center stimulus placed in the far surround most often suppressed the RF center’s

response, while less suppression or sometimes facilitation was observed for cross-oriented center and surround stimuli (DeAngelis et al., 1994; Sillito et al., 1995; Levitt and Lund, 1997, 2002; Sengpiel et al., 1998; Sceniak et al., 2001; Cavanaugh et al., 2002). Center–surround interactions in these studies were well fit with a difference of Gaussians (DOG) model (Fig. 2b), i.e. they could be modeled as the summation of a center excitatory Gaussian, corresponding to the lsRF, overlapping a spatially more extensive surround inhibitory Gaussian, corresponding to the suppressive surround (for a review see Shapley, 2004). The DOG model predicts that large gratings placed in the far surround suppress the cell’s response to optimal gratings in the center; this model is inconsistent with facilitation arising from the far surround. While most studies in primates have observed suppression for iso-oriented gratings in the RF center and in the far

surround, later in this chapter we demonstrate that in primate V1, under appropriate stimulus conditions, stimuli in the far surround of the same orientation as the center stimulus can also facilitate responses to optimally oriented stimuli in the RF center.

In other studies, the RF center size was defined as the neuron’s mRF, and the surround as the region outside it. In this chapter we refer to the region outside the mRF, but within the lsRF, as the ‘‘near’’ surround (Fig. 2a, right). In these studies, iso-oriented spatially discrete stimuli, such as bars or Gabor patches, placed in the center and in the near surround often facilitated the RF center’s response (Kapadia et al., 1995; Polat et al., 1998; Mizobe et al., 2001; Chisum et al., 2003). A specific case of such short-range modulation is ‘‘collinear facilitation’’, an enhancement of the mRF response to an optimally oriented bar (or

Gabor patch) stimulus by flanking co-oriented and co-axial high-contrast bar (or Gabor) stimuli (Fig. 2a, right). This phenomenon is thought to underlie perceptual ‘‘grouping’’ of contour elements (Kapadia et al., 1995; Hess and Field, 2000). In many instances, this short range modulation has been shown to be contrast dependent (Polat et al., 1998; Mizobe et al., 2001), with surround facilitation of low-contrast center stimuli turning to suppression for high-contrast center stimuli. We (Angelucci and Bullier, 2003) have previously suggested that in most of these studies the stimuli outside the mRF most likely involved the lsRF of the cell (or near

surround), i.e. the center excitatory mechanism of the DOG model (Fig. 2b). In other words, near surround facilitation could reflect the same mechanism underlying the expansion of the sRF at low stimulus contrast. This interpretation is consistent with the DOG model. However, it appears that not all short-range surround modulation effects are consistent with this interpretation, as several other types of short-range interactions, both facilitatory and suppressive have been reported for different center stimulus contrasts and different center and surround stimulus configurations (Chen et al., 2001; Mizobe et al., 2001). Furthermore, later in this

chapter we demonstrate that iso-orientation surround facilitation can also be induced by stimuli in the far surround.

Using single unit extracellular recordings, we (Angelucci et al., 2002b) and others (Sceniak et al., 2001; Cavanaugh et al., 2002; Levitt and Lund, 2002) have measured the extent of the far surround of macaque V1 neurons by presenting highcontrast (75%) gratings of increasing diameter to the cell, and defining the surround size as the stimulus diameter at asymptotic response (black arrow in Fig. 1a). Between 21 and 81 eccentricity, V1 cells’ surround sizes averaged 5.1170.6, ranging up to 131 (largest stimulus size we tested; Fig. 1c), and were on average 4.6 times larger than the same cells’ hsRF size (Fig. 1d). More recently, using larger surround gratings, we have found neurons in macaque V1 with even larger surround diameter, i.e. up to 281 (Ichida et al., 2005).

Candidate anatomical substrates for the receptive field center and surround of V1 neurons

Feedforward (FF) connections to V1 from the lateral geniculate nucleus (LGN), long-range V1 lateral (or horizontal) connections, and feedback (FB) connections to V1 from extrastriate cortex, could all contribute to generating the RF center and surround of V1 neurons. In the following sections, we review previous and more recent studies on the basic properties of these systems of connections and, on the basis of such properties, provide a hypothesis on the relative contributions of each connection type to the RF center and surround of V1 cells.

Area V1 receives its main driving FF inputs from the LGN of the thalamus. The geniculocortical pathway in the macaque consists of at least three channels, magnocellular (Magno), parvocellular (Parvo) and koniocellular (K) or interlaminar, originated in the retina from different ganglion cell populations (for reviews see: Casagrande, 1994; Hendry and Reid, 2000; Casagrande and Xu, 2004; Kaplan, 2004). LGN axons belonging to these different channels terminate in different V1 layers (Fig. 3), with the relays from each eye segregated into interleaved terminal stripe-like territories,

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or ocular dominance bands (for reviews see: Casagrande and Kaas, 1994; Levitt et al., 1996). The principal termination zones of Magno thalamic axons are layers 4Ca and 6; Parvo thalamic afferents terminate in layers 4A, 4Cb and 6, while K afferents terminate in layer 1 and in the cytochrome oxidase (CO)-rich regions (the CO blobs) of layer 3 (Hubel and Wiesel, 1972; Hendrickson et al., 1978; Lund, 1988; Hendry and Yoshioka, 1994; Yazar et al., 2004).

Geniculocortical connections are spatially restricted, i.e. they connect discrete regions of the LGN and V1 (Perkel et al., 1986). Such precise organization represents the anatomical basis for the orderly retinotopic map seen in the responses of postsynaptic neurons in input layer 4C (Blasdel and Fitzpatrick, 1984). Unlike in cats, where most cells in layer 4 are orientation selective (Hubel and Wiesel, 1962), in primates many cells in layer 4C have circularly symmetric LGN-like RFs that are not tuned, or are broadly tuned for orientation (Poggio et al., 1977; Bauer et al., 1980; Bullier and Henry, 1980; Blasdel and Fitzpatrick, 1984; Hawken and Parker, 1984; Leventhal et al., 1995). Orientation-selective neurons in macaque, however, first appear within layer 4C (Ringach et al., 2002; Gur et al., 2005). The generation of orientation specificity is generally thought to partly involve the convergence to single neurons of retinotopic information distributed along the axis of the neurons’ preferred orientation (Hubel and Wiesel, 1962). In cats, this convergence is believed to be from LGN axons to single layer 4 neurons (for a review see Reid and Usrey, 2004); however, in macaque it is more likely to occur within layer 4C itself (Lund et al., 1995; Adorjan et al., 1999; Lund et al., 2003). In the macaque, FF afferents from the LGN could instead contribute to generating the non-oriented, circularly symmetric RFs of layer 4C.

Neurons in layer 4C show a gradient of decreasing mRF size and contrast sensitivity from the top to the bottom of the layer (Blasdel and Fitzpatrick, 1984; Hawken and Parker, 1984). Individual thalamic axons belonging to the different channels show very different terminal arbor size, with the Magno axons terminating at the top of layer 4Ca showing the largest intracortical

spread, and the Parvo axons terminating in layer 4Cb showing the smallest spread (Blasdel and Lund, 1983; Freund et al., 1989). Modeling studies have suggested that feedforward convergence of Parvo and Magno inputs onto layer 4C spiny stellate neurons is sufficient to explain the observed

gradual change in mRF size and contrast sensitivity seen along the depth of this layer (Bauer et al., 1999).

In summary, because of all the properties described above, and because they drive their target V1 cells, geniculate FF afferents have traditionally