Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-23T10:43:00.778Z Has data issue: false hasContentIssue false
  • English
  • Français

Quadrature subunits in directionally selective simple cells: Spatiotemporal interactions

Published online by Cambridge University Press:  02 June 2009

Robert C. Emerson
Robert C. Emerson
Affiliation:
Department of Ophthalmology and Center for Visual Science, University of Rochester, Rochester, New York
Get access Rights & Permissions [Opens in a new window]

Abstract

I explore here whether linear mechanisms can explain directional selectivity (DS) in simple cells of the cat's striate cortex, a question suggested by a recent upswing in popularity of linear DS models. I chose a simple cell with a space-time inseparable receptive field (RF), i.e. one that shows gradually shifting latency across space, as the RF type most likely to depend on linear mechanisms of DS. However, measured responses of the cell to a moving bar were less modulated, and extended over a larger spatial region than predicted by two different popular “linear” models. They also were more DS in exhibiting a higher ratio of total spikes for the preferred direction. Each of the two models used for comparison has a single “branch” with a single spatiotemporally inseparable linear filter followed by a threshold, hence, a “1-branch” model. Nonlinear interactions between pairs of bars in a 2-bar linear superposition test of the cell also disagreed in time-course with those of the 1-branch models. The only model whose 1-bar and 2-bar predictions matched the measured cell (including a complete “4-branch” motion energy model that matches complex cells) has two branches that differ in phase by about 90 deg, i.e. in quadrature. Each branch has its own threshold that helps define the preceding spatiotemporal unit as a subunit even after the outputs of the two branches are summed. As subunit phases differ by only 90 deg, flashing bar responses of the 2-subunit model are similar to those of the 1-subunit model. Therefore, the number of subunits is hidden from view when testing with a conventional stationary bar. In summary, movement responses and nonlinear interactions between pairs of bars in the measured cell matched those of the 2-subunit model, while they disagreed with the popular 1-subunit model. Thus, multiple nonlinear subunits appear to be necessary for DS, even in simple cortical cells.

Keywords

Directional selectivityReceptive-field subunitsNonlinear mechanismsSimple cellsVisual cortex
Type
Research Articles
Information
Visual Neuroscience , Volume 14 , Issue 2 , March 1997 , pp. 357 - 371
Copyright
Copyright © Cambridge University Press 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adelson, E.H. & Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A 2, 284–299.CrossRefGoogle ScholarPubMed
Albrecht, D.G. & Geisler, W.S. (1991). Motion selectivity and the contrast-response function of simple cells in the visual cortex. Visual Neuroscience 7, 531–546.CrossRefGoogle ScholarPubMed
Burr, D.C., Ross, J. & Morrone, M.C. (1986). Seeing objects in motion. Proceedings of the Royal Society B (London) 227, 249–265.Google ScholarPubMed
Carandini, M. & Heeger, D.J. (1994). Summation and division by neurons in primate visual cortex. Science 264, 1333–1336.CrossRefGoogle ScholarPubMed
Citron, M.C., Emerson, R.C. & Ide, L.S. (1981). Spatial and temporal receptive-field analysis of the cat's geniculocortical pathway. Vision Research 21, 385–397.CrossRefGoogle ScholarPubMed
Citron, M.C. & Emerson, R.C. (1983). White noise analysis of conical directional selectivity in cat. Brain Research 279, 271–277.CrossRefGoogle Scholar
Citron, M.C., Emerson, R.C. & Levick, W.R. (1988). Nonlinear measurement and classification of receptive fields in cat retinal ganglion cells. Annals of Biomedical Engineering 16, 65–77.CrossRefGoogle ScholarPubMed
DeAngelis, G.C., Ohzawa, I. & Freeman, R.D. (1993 a). Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. Journal of Neurophysiology 69, 1091–1117.CrossRefGoogle ScholarPubMed
DeAngelis, G.C., Ohzawa, I. & Freeman, R.D. (1993 b). Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. Journal of Neurophysiology 69, 1118–1135.CrossRefGoogle ScholarPubMed
Emerson, R.C. & Coleman, L. (1981). Does image movement have a special nature for neurons in the cat's striate cortex? Investigative Ophthalmology and Visual Science 20, 766–783.Google ScholarPubMed
Emerson, R.C. & Gerstein, G.L. (1977 a). Simple striate neurons in the cat. I. Comparison of responses to moving and stationary stimuli. Journal of Neurophysiology 40, 119–135.CrossRefGoogle ScholarPubMed
Emerson, R.C. & Gerstein, G.L. (1977 b). Simple striate neurons in the cat. II. Mechanisms underlying directional asymmetry and directional selectivity. Journal of Neurophysiology 40, 136–155.CrossRefGoogle ScholarPubMed
Emerson, R.C., Citron, M.C., Felleman, D.J. & Kaas, J.H. (1985). A proposed mechanism and site for cortical directional selectivity. In Models of the Visual Cortex, ed. Rose, D. & Dobson, V.G., pp. 420–431. Chichester, England: John Wiley & Sons.Google Scholar
Emerson, R.C. & Vaughn, W.J. (1987). Nonlinear spatiotemporal analysis of directionally selective cortical neurons. In Advanced Methods of Physiological System Modeling, ISBN 0–941639–00–2, ed. Marmare-lis, V.Z., pp. 172–204. Los Angeles, California: Biomedical Simulations Resource.Google Scholar
Emerson, R.C., Citron, M.C., Vaughn, W.J. & Klein, S.A. (1987). Nonlinear directionally selective subunits in complex cells of cat striate cortex. Journal of Neurophysiology 58, 33–65.CrossRefGoogle ScholarPubMed
Emerson, R.C. (1988). A linear model for symmetric receptive fields: Implications for classification tests with flashed and moving images. Spatial Vision 3, 159–177.CrossRefGoogle ScholarPubMed
Emerson, R.C., Korenberg, M.J. & Citron, M.C. (1989). Identification of intensive nonlinearities in cascade models of visual cortex and its relation to cell classification. In Advanced Methods of Physiological System Modeling, ISBN 0–306–43259–5, ed. Marmarelis, V.Z., pp. 97–111. New York: Plenum Press.CrossRefGoogle Scholar
Emerson, R.C. & Citron, M.C. (1992). Linear and nonlinear mechanisms of motion selectivity in simple cells of the cat's striate cortex. In Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks, ed. Pinter, R.B. & Nabet, B., pp. 75–89. Boca Raton, Florida: CRC Press.Google Scholar
Emerson, R.C., Korenberg, M.J. & Citron, M.C. (1992 a). Identification of complex-cell intensive nonlinearities in a cascade model of cat visual cortex. Biological Cybernetics 66, 291–300.CrossRefGoogle Scholar
Emerson, R.C., Bergen, J.R. & Adelson, E.H. (1992 b). Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Research 32, 203–218.CrossRefGoogle ScholarPubMed
Emerson, R.C. & Citron, M.C. (1993). Nonlinear subunits in directionally selective spatiotemporally inseparable simple cells. Investigative Ophthalmology and Visual Science (Suppl.) 34, 907.Google Scholar
Emerson, R.C. & Maher, S.J. (1994). Hidden subunits may explain strength of directional selectivity in simple cells of striate cortex. Investigative Ophthalmology and Visual Science (Suppl.) 35, 1469.Google Scholar
Emerson, R.C. & Vaughn, W.J. (1996). Measuring space-time inseparability in motion selective cortical neurons. Investigative Ophthalmology and Visual Science (Suppl.) 37, S904.Google Scholar
Emerson, R.C. & Huang, M.C. (1997). Quadrature subunits in directionally selective simple cells: Counterphase and drifting grating responses. Visual Neuroscience 14, 373–385.CrossRefGoogle ScholarPubMed
Ferster, D. (1988). Spatially opponent excitation and inhibition in simple cells of the cat visual cortex. Journal of Neuroscience 8, 1172–1180.CrossRefGoogle ScholarPubMed
Ferster, D. & Jagadeesh, B. (1992). EPSP-IPSP interactions in cat visual cortex studied with in vivo whole-cell patch recording. Journal of Neuroscience 12, 1262–1274.CrossRefGoogle ScholarPubMed
Ganz, L. & Felder, R. (1984). Mechanism of directional selectivity in simple neurons of the cat's visual cortex analyzed with stationary flash sequences. Journal of Neurophysiology 51, 294–324.CrossRefGoogle ScholarPubMed
Gaska, J.P., Jacobson, L.D., Chen, H. & Pollen, D.A. (1994). Space-time spectra of complex cell filters in the in macaque monkey: A comparison of results obtained with pseudowhite noise and grating stimuli. Visual Neuroscience 11, 805–821.CrossRefGoogle ScholarPubMed
Goodwin, A.W., Henry, G.H. & Bishop, P.O. (1975). Direction selectivity of simple striate cells: Properties and mechanism. Journal of Neurophysiology 38, 1500–1523.CrossRefGoogle ScholarPubMed
Hamilton, D.B., Albrecht, D.G. & Geisler, W.S. (1989). Visual cortical receptive fields in monkey and cat: Spatial and temporal phase transfer function. Vision Research 29, 1285–1308.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1992 a). Normalization of cell responses in cat striate cortex. Visual Neuroscience 9, 181–197.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1992 b). Half-squaring in responses of cat striate cells. Visual Neuroscience 9, 427–443.CrossRefGoogle ScholarPubMed
Heeger, D.J. (1993). Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. Journal of Neurophysiology 70, 1885–1898.CrossRefGoogle ScholarPubMed
Heggelund, P. (1981). Receptive field organization of simple cells in cat striate cortex. Experimental Brain Research 42, 89–98.Google ScholarPubMed
Hubel, D.H. & Wiesel, T.N. (1959). Receptive fields of single neurones in the cat's striate cortex. Journal of Physiology (London) 148, 574–591.CrossRefGoogle ScholarPubMed
Hubel, D.H. & Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology (London) 160, 106–154.CrossRefGoogle ScholarPubMed
Humphrey, A.L. & Weller, R.E. (1988 a). Functionally distinct groups of X-cells in the lateral geniculate nucleus of the cat. Journal of Comparative Neurology 268, 429–447.CrossRefGoogle ScholarPubMed
Humphrey, A.L. & Weller, R.E. (1988 b). Structural correlates of functionally distinct X-cells in the lateral geniculate nucleus of the cat. Journal of Comparative Neurology 268, 448–468.CrossRefGoogle ScholarPubMed
Hunter, I.W. & Korenberg, M.J. (1986). The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biological Cybernetics 55, 135–144.CrossRefGoogle ScholarPubMed
Ikeda, H. & Wright, M.J. (1975). Spatial and temporal properties of ‘sustained’ and ‘transient’ neurones in area 17 of the cat's visual cortex. Experimental Brain Research 22, 363–383.Google Scholar
Jacobson, L.D., Gaska, J.P., Chen, H. & Pollen, D.A. (1993). Structural testing of multi-input linear-nonlinear cascade models for cells in macaque striate cortex. Vision Research 33, 609–626.CrossRefGoogle ScholarPubMed
Jagadeesh, B., Wheat, H.S. & Ferster, D. (1993). Linearity of summation of synaptic potentials underlying direction selectivity in simple cells of cat visual cortex. Science 262, 1901–1904.CrossRefGoogle ScholarPubMed
Kontsevich, L.L. (1995). The nature of the inputs to cortical motion detectors. Vision Research 19, 2785–2793.CrossRefGoogle Scholar
Korenberg, M. (1973). Identification of biological cascades of linear and static nonlinear systems. Proceedings of the 16th Midwest Symposium on Circuit Theory XVIII.2, 1–9.Google Scholar
Korenberg, M., Sakai, H.M. & Naka, K. (1989). Dissection of the neuron network in the catfish inner retina, III. Interpretation of spike kernels. Journal of Neurophysiology 61, 1110–1120.CrossRefGoogle ScholarPubMed
Kulikowski, J.J. & Bishop, P.O. (1981). Linear analysis of the responses of simple cells in the cat striate cortex. Experimental Brain Research 44, 386–400.CrossRefGoogle Scholar
Larkin, R.M., Klein, S., Ogden, T.E. & Fender, D.H. (1979). Nonlinear kernels of the human ERG. Biological Cybernetics 35, 145–160.CrossRefGoogle ScholarPubMed
Levine, M.W. & Abramov, I. (1975). An analysis of spatial summation in the receptive fields of goldfish retinal ganglion cells. Vision Research 15, 777–789.CrossRefGoogle ScholarPubMed
Liu, Z., Gaska, J.R., Jacobson, L.D. & Pollen, D.A. (1992). Interneuronal interaction between members of quadrature phase and anti-phase pairs in the cat's visual cortex. Vision Research 32, 1193–1198.CrossRefGoogle ScholarPubMed
Marmarelis, P.Z. & Marmarelis, V.Z. (1978). In Analysis of Physiological Systems: The White-Noise Approach. New York: Plenum Press.CrossRefGoogle Scholar
Mastronarde, D.N. (1987 a). Two classes of single-input X-cells in cat lateral geniculate nucleus. I. Receptive-field properties and the classification of cells. Journal of Neurophysiology 57, 357–380.CrossRefGoogle ScholarPubMed
Mastronarde, D.N. (1987 b). Two classes of single-input X-cells in cat lateral geniculate nucleus. II. Retinal inputs and the generation of receptive-field properties. Journal of Neurophysiology 57, 381–413.CrossRefGoogle ScholarPubMed
McLean, J. & Palmer, L.A. (1989). Contribution of linear spatiotemporal receptive field structure to velocity selectivity of simple cells in area 17 of cat. Vision Research 29, 675–679.CrossRefGoogle ScholarPubMed
McLean, J., Raab, S. & Palmer, L.A. (1994). Contribution of linear mechanisms to the specification of local motion by simple cells in areas 17 and 18 of the cat. Visual Neuroscience 11, 271–294.CrossRefGoogle Scholar
Movshon, J.A., Thompson, I.D. & Tolhurst, D.J. (1978). Spatial summation in receptive fields of simple cells in the cat's striate cortex. Journal of Physiology (London) 283, 53–77.CrossRefGoogle ScholarPubMed
Pollen, D.A., Gaska, J.P. & Jacobson, L.D. (1989). Physiological constraints on models of visual cortical function. In Models of brain function, ed. Cotterill, R.M.J., pp. 115–135. Cambridge, UK: Cambridge University Press.Google Scholar
Raymond, J. & Braddick, O.J. (1996). Responses to opposed directions of motion: Continuum or independent mechanisms? Vision Research 36, 1931–1937.CrossRefGoogle ScholarPubMed
Reid, R.C., Soodak, R.E. & Shapley, R.M. (1987). Linear mechanisms of directional selectivity in simple cells of cat striate cortex. Proceedings of the National Academy of Sciences of the U.S.A. 84, 8740–8744.CrossRefGoogle ScholarPubMed
Reid, R.C., Soodak, R.E. & Shapley, R.M. (1991). Directional selectivity and spatiotemporal structure of receptive fields of simple cells in cat striate cortex. Journal of Neurophysiology 66, 505–529.CrossRefGoogle ScholarPubMed
Rodieck, R.W. (1965). Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Research 5, 583–601.CrossRefGoogle ScholarPubMed
Saul, A.B. & Humphrey, A.L. (1990). Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. Journal of Neurophysiology 64, 206–224.CrossRefGoogle ScholarPubMed
Saul, A.B. & Humphrey, A.L. (1992 a). Evidence of input from lagged cells in the lateral geniculate nucleus to simple cells in cortical area 17 of the cat. Journal of Neurophysiology 68, 1190–1208.CrossRefGoogle ScholarPubMed
Saul, A.B. & Humphrey, A.L. (1992 b). Temporal-frequency tuning of direction selectivity in cat visual cortex. Visual Neuroscience 8, 365–372.CrossRefGoogle ScholarPubMed
Tadmor, Y. & Tolhurst, D.J. (1989). The effect of threshold on the relationship between the receptive-field profile and the spatial-frequency tuning curve in simple cells of the cat's striate cortex. Visual Neuroscience 3, 445–454.CrossRefGoogle ScholarPubMed
Watson, A.B. & Ahumada, A.J. (1983). A look at motion in the frequency domain. NASA Technical Paper 84352, 1–10.Google Scholar
Watson, A.B. & Ahumada, A.J. (1985). Model of human visual-motion sensing. Journal of the Optical Society of America A 2, 322–342.CrossRefGoogle ScholarPubMed