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V1 orientation plasticity is explained by broadly tuned feedforward inputs and intracortical sharpening

Published online by Cambridge University Press:  16 April 2010

ANDREW F. TEICH*
Affiliation:
Department of Pathology, Columbia University, New York, New York Department of Neuroscience, Columbia University, New York, New York
NING QIAN
Affiliation:
Department of Neuroscience, Columbia University, New York, New York Department of Physiology and Cellular Biophysics, Columbia University, New York, New York
*
*Address correspondence and reprint requests to: Dr. Andrew F. Teich, Division of Neuropathology, Department of Pathology, Columbia University, 630 West 168th Street, PH 15 Stem—Room 124, New York, NY 10032. E-mail: [email protected]

Abstract

Orientation adaptation and perceptual learning change orientation tuning curves of V1 cells. Adaptation shifts tuning curve peaks away from the adapted orientation, reduces tuning curve slopes near the adapted orientation, and increases the responses on the far flank of tuning curves. Learning an orientation discrimination task increases tuning curve slopes near the trained orientation. These changes have been explained previously in a recurrent model (RM) of orientation selectivity. However, the RM generates only complex cells when they are well tuned, so that there is currently no model of orientation plasticity for simple cells. In addition, some feedforward models, such as the modified feedforward model (MFM), also contain recurrent cortical excitation, and it is unknown whether they can explain plasticity. Here, we compare plasticity in the MFM, which simulates simple cells, and a recent modification of the RM (MRM), which displays a continuum of simple-to-complex characteristics. Both pre- and postsynaptic-based modifications of the recurrent and feedforward connections in the models are investigated. The MRM can account for all the learning- and adaptation-induced plasticity, for both simple and complex cells, while the MFM cannot. The key features from the MRM required for explaining plasticity are broadly tuned feedforward inputs and sharpening by a Mexican hat intracortical interaction profile. The mere presence of recurrent cortical interactions in feedforward models like the MFM is insufficient; such models have more rigid tuning curves. We predict that the plastic properties must be absent for cells whose orientation tuning arises from a feedforward mechanism.

Type
Research Articles
Copyright
Copyright © Cambridge University Press 2010

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References

Abbott, L.F. & Nelson, S.B. (2000). Synaptic plasticity: Taming the beast. Nature Neuroscience 3(Suppl.), 11781183.Google Scholar
Albrecht, D.G., Farrar, S.B. & Hamilton, D.B. (1984). Spatial contrast adaptation characteristics of neurones recorded in the cat’s visual cortex. The Journal of Physiology 347, 713739.Google Scholar
Ben-Yishai, R., Bar-Or, R.L. & Sompolinsky, H. (1995). Theory of orientation tuning in visual cortex. Proceedings of the National Academy of Sciences of the United States of America 92, 38443848.Google Scholar
Betz, W.J. (1970). Depression of transmitter release at the neuromuscular junction of the frog. The Journal of Physiology 206, 629644.Google Scholar
Bonds, A.B. (1991). Temporal dynamics of contrast gain in single cells of the cat striate cortex. Visual Neuroscience 6, 239255.Google Scholar
Carandini, M. & Ferster, D. (1997). A tonic hyperpolarization underlying contrast adaptation in cat visual cortex. [see comment]. Science 276, 949952.Google Scholar
Carandini, M. & Ringach, D.L. (1997). Predictions of a recurrent model of orientation selectivity. Vision Research 37, 30613071.Google Scholar
Chen, Y., Wang, Y. & Qian, N. (2001). Modeling V1 disparity tuning to time-varying stimuli. Journal of Neurophysiology 86, 143155.Google Scholar
Chung, S. & Ferster, D. (1998). Strength and orientation tuning of the thalamic input to simple cells revealed by electrically evoked cortical suppression. Neuron 20, 11771189.Google Scholar
Clifford, C.W., Wenderoth, P. & Spehar, B. (2000). A functional angle on some after-effects in cortical vision. Proceedings of the Royal Society of London. Series B, Biological Sciences 267, 17051710.Google Scholar
Dean, A.F. (1983). Adaptation-induced alteration of the relation between response amplitude and contrast in cat striate cortical neurones. Vision Research 23, 249256.Google Scholar
Douglas, R.J., Koch, C., Mahowald, M., Martin, K.A. & Suarez, H.H. (1995). Recurrent excitation in neocortical circuits. Science 269, 981985.CrossRefGoogle ScholarPubMed
Dragoi, V., Rivadulla, C. & Sur, M. (2001). Foci of orientation plasticity in visual cortex. Nature 411, 8086.CrossRefGoogle ScholarPubMed
Dragoi, V., Sharma, J., Miller, E.K. & Sur, M. (2002). Dynamics of neuronal sensitivity in visual cortex and local feature discrimination. Nature Neuroscience 5, 883891.Google Scholar
Dragoi, V., Sharma, J. & Sur, M. (2000). Adaptation-induced plasticity of orientation tuning in adult visual cortex. Neuron 28, 287298.CrossRefGoogle ScholarPubMed
Feldman, D.E. (2002). Synapses, scaling and homeostasis in vivo. [comment]. Nature Neuroscience 5, 712714.Google Scholar
Felsen, G., Shen, Y.S., Yao, H., Spor, G., Li, C. & Dan, Y. (2002). Dynamic modification of cortical orientation tuning mediated by recurrent connections. Neuron 36, 945954.Google Scholar
Ferster, D., Chung, S. & Wheat, H. (1996). Orientation selectivity of thalamic input to simple cells of cat visual cortex. [see comment]. Nature 380, 249252.CrossRefGoogle ScholarPubMed
Ferster, D. & Miller, K.D. (2000). Neural mechanisms of orientation selectivity in the visual cortex. Annual Review of Neuroscience 23, 441471.Google Scholar
Ghose, G.M., Yang, T. & Maunsell, J.H. (2002). Physiological correlates of perceptual learning in monkey V1 and V2. Journal of Neurophysiology 87, 18671888.Google Scholar
Giaschi, D., Douglas, R., Marlin, S. & Cynader, M. (1993). The time course of direction-selective adaptation in simple and complex cells in cat striate cortex. Journal of Neurophysiology 70, 20242034.Google Scholar
Gibson, J.J. (1933). Adaptation, after-effect and contrast in the perception of curved lines. Journal of Experimental Psychology 16, 133.Google Scholar
Gilbert, C.D. & Wiesel, T.N. (1990). The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat. Vision Research 30, 16891701.Google Scholar
Greenlee, M.W. & Magnussen, S. (1987). Saturation of the tilt aftereffect. Vision Research 27, 10411043.Google Scholar
Hammond, P., Mouat, G.S. & Smith, A.T. (1988). Neural correlates of motion after-effects in cat striate cortical neurones: Monocular adaptation. Experimental Brain Research 72, 120.Google Scholar
Hubel, D.H. & Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160, 106154.Google Scholar
Jin, D.Z., Dragoi, V., Sur, M. & Seung, H.S. (2005). Tilt aftereffect and adaptation-induced changes in orientation tuning in visual cortex. Journal of Neurophysiology 94, 40384050.CrossRefGoogle ScholarPubMed
Kohn, A. & Movshon, J.A. (2004). Adaptation changes the direction tuning of macaque MT neurons. Nature Neuroscience 7, 764772.CrossRefGoogle ScholarPubMed
Kusano, K. & Landau, E.M. (1975). Depression and recovery of transmission at the squid giant synapse. The Journal of Physiology 245, 1322.CrossRefGoogle ScholarPubMed
Maffei, L., Fiorentini, A. & Bisti, S. (1973). Neural correlate of perceptual adaptation to gratings. Science 182, 10361038.Google Scholar
Marlin, S.G., Hasan, S.J. & Cynader, M.S. (1988). Direction-selective adaptation in simple and complex cells in cat striate cortex. Journal of Neurophysiology 59, 13141330.Google Scholar
Movshon, J.A. & Lennie, P. (1979). Pattern-selective adaptation in visual cortical neurones. Nature 278, 850852.Google Scholar
Muller, J.R., Metha, A.B., Krauskopf, J. & Lennie, P. (1999). Rapid adaptation in visual cortex to the structure of images. Science 285, 14051408.Google Scholar
Ohzawa, I., Sclar, G. & Freeman, R.D. (1982). Contrast gain control in the cat visual cortex. Nature 298, 266268.Google Scholar
Ohzawa, I., Sclar, G. & Freeman, R.D. (1985). Contrast gain control in the cat’s visual system. Journal of Neurophysiology 54, 651667.CrossRefGoogle ScholarPubMed
Peters, A. & Payne, B.R. (1993). Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. Cerebral Cortex 3, 6978.Google Scholar
Qian, N. & Matthews, N. (1999). A physiological theory for visual perceptual learning of orientation discrimination. Society for Neuroscience Abstract 25, 1316.Google Scholar
Raiguel, S., Vogels, R., Mysore, S.G. & Orban, G.A. (2006). Learning to see the difference specifically alters the most informative V4 neurons. [see comment]. Journal of Neuroscience 26, 65896602.Google Scholar
Regan, D. & Beverley, K.I. (1985). Postadaptation orientation discrimination. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 2, 147155.Google Scholar
Schoups, A., Vogels, R., Qian, N. & Orban, G. (2001). Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549553.Google Scholar
Schoups, A.A., Vogels, R. & Orban, G.A. (1998). Effects of perceptual learning in orientation discrimination on orientation coding in v1. Investigative Ophthalmology & Visual Science 39, 684.Google Scholar
Sclar, G., Lennie, P. & DePriest, D.D. (1989). Contrast adaptation in striate cortex of macaque. Vision Research 29, 747755.Google Scholar
Somers, D.C., Nelson, S.B. & Sur, M. (1995). An emergent model of orientation selectivity in cat visual cortical simple cells. Journal of Neuroscience 15, 54485465.Google Scholar
Sutherland, N.S. (1961). Figural after-effects and apparent size. Quarterly Journal of Experimental Psychology 13, 222228.CrossRefGoogle Scholar
Teich, A.F. & Qian, N. (2003 a). Learning and adaptation in a recurrent model of V1 orientation selectivity. Journal of Neurophysiology 89, 20862100.Google Scholar
Teich, A.F. & Qian, N. (2003 b). Modeling Learning- and Adaptation-Induced Changes to V1 Orientation Tuning. Program No. 126.9. 2003 Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience. Online.Google Scholar
Teich, A.F. & Qian, N. (2006). Comparison among some models of orientation selectivity. Journal of Neurophysiology 96, 404419.Google Scholar
Teich, A.F. & Qian, N. (2007). Modeling Learning and Adaptation Induced Plasticity of Orientation Tuning in V1. Poster III-110. COSYNE 2007 Abstract Book.Google Scholar
Troyer, T.W., Krukowski, A.E., Priebe, N.J. & Miller, K.D. (1998). Contrast-invariant orientation tuning in cat visual cortex: Thalamocortical input tuning and correlation-based intracortical connectivity. Journal of Neuroscience 18, 59085927.Google Scholar
Turrigiano, G.G. & Nelson, S.B. (2000). Hebb and homeostasis in neuronal plasticity. Current Opinion in Neurobiology 10, 358364.Google Scholar
Vidyasagar, T.R. & Siguenza, J.A. (1985). Relationship between orientation tuning and spatial frequency in neurones of cat area 17. Experimental Brain Research 57, 628631.Google Scholar
von der Heydt, R., Hanny, P. & Adorjani, C. (1978). Movement aftereffects in the visual cortex. Archives Italiennes de Biologie 116, 248254.Google Scholar
Wolfe, J.M. (1984). Short test flashes produce large tilt aftereffects. Vision Research 24, 19591964.Google Scholar
Yang, T. & Maunsell, J.H. (2004). The effect of perceptual learning on neuronal responses in monkey visual area V4. Journal of Neuroscience 24, 16171626.CrossRefGoogle ScholarPubMed
Yao, H. & Dan, Y. (2001). Stimulus timing-dependent plasticity in cortical processing of orientation. Neuron 32, 315323.CrossRefGoogle ScholarPubMed