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Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex
Open accessAbstract
The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a given synapse depends not only on instantaneous pre- and postsynaptic activities but also on a slowly varying time-averaged value of the postsynaptic activity. Assuming an appropriate nonlinear form for this dependence, development of selectivity is obtained under quite general conditions on the sensory environment. One does not require nonlinearity of the neuron's integrative power nor does one need to assume any particular form for intracortical circuitry. This is first illustrated in simple cases, e.g., when the environment consists of only two different stimuli presented alternately in a random manner. The following formal statement then holds: the state of the system converges with probability 1 to points of maximum selectivity in the state space. We next consider the problem of early development of orientation selectivity and binocular interaction in primary visual cortex. Giving the environment an appropriate form, we obtain orientation tuning curves and ocular dominance comparable to what is observed in normally reared adult cats or monkeys. Simulations with binocular input and various types of normal or altered environments show good agreement with the relevant experimental data. Experiments are suggested that could test our theory further.
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“Consistent with original ideas (Bienenstock et al. 1982), modest depolarization can elicit LTD, whereas stronger depolarization leads to LTP.”
From NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD). (2012) · cites this paper as Bienenstock et al. 1982
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“If this were the case, it has been argued that the system would be unstable and quickly saturate (366–371).”
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“The existence of LTD avoids the problem of saturation, which could be essential for a mnemonic device (366–371).”
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Cites
SINGLE-CELL RESPONSES IN STRIATE CORTEX OF KITTENS DEPRIVED OF VISION IN ONE EYE
Wiesel, T. N., & Hubel, D. H. (1963) · Journal of Neurophysiology · doi:10.1152/jn.1963.26.6.1003 · Cited by 2,628
Self-organization of orientation sensitive cells in the striate cortex
Malsburg, C. V. D. (1973) · Kybernetik · doi:10.1007/bf00288907 · Cited by 1,444
RECEPTIVE FIELDS OF CELLS IN STRIATE CORTEX OF VERY YOUNG, VISUALLY INEXPERIENCED KITTENS
Hubel, D. H., & Wiesel, T. N. (1963) · Journal of Neurophysiology · doi:10.1152/jn.1963.26.6.994 · Cited by 1,113
A Physiological Mechanism for Hebb's Postulate of Learning
Stent, G. S. (1973) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.70.4.997 · Cited by 710
EXTENT OF RECOVERY FROM THE EFFECTS OF VISUAL DEPRIVATION IN KITTENS
Wiesel, T. N., & Hubel, D. H. (1965) · Journal of Neurophysiology · doi:10.1152/jn.1965.28.6.1060 · Cited by 704
Storing covariance with nonlinearly interacting neurons
Sejnowski, T. J. (1977) · Journal of Mathematical Biology · doi:10.1007/bf00275079 · Cited by 546
Kasamatsu, T., & Pettigrew, J. D. (1976) · Science · doi:10.1126/science.959850 · Cited by 436
Kasamatsu, T., & Pettigrew, J. D. (1979) · The Journal of Comparative Neurology · doi:10.1002/cne.901850109 · Cited by 314
Imbert, M., & Buisseret, P. (1975) · Experimental Brain Research · doi:10.1007/bf00235409 · Cited by 303
Rauschecker, J. P., & Singer, W. (1981) · The Journal of Physiology · doi:10.1113/jphysiol.1981.sp013545 · Cited by 220
Research works printed but not linked: 7
- Anderson, J. A. (1970) Two models for memory organization Hebb, D. 0. (1949) Organization of Behavior, John Wiley and using interacting traces. Math. Biosci. 8: 137-160.
- selectivity. Doctoral thesis, Brown University, Providence, Hirsch, H. V. B., and D. N. Spinelli (1971) Modification of the RI. distribution of receptive field orientation in cats by selective
- Hubel, D. H., and T. N. Wiesel (1962) Receptive fields, binoc- Vol. 2, No. 1, Jan. 1982 ular interaction and functional architecture in the cat's visual cortex. J. Physiol. (Lond.) 160: 106-154.
- LeventhaI, A. G., and H. V. B. Hirsch (1977) Effects of early experience upon orientation selectivity and binocularity of neurons in visual cortex of cats. Proc. Natl. Acad. Sci. U. S. A. 74: 1272-1276.
- Pettigrew, J. D. (1978) The paradox of the critical period for striate cortex. In Neuronal Plasticity, C. W. Cotman, ed., pp. 311-330, Raven Press, New York.
- Singer, W. (1979) Central-core control of visual functions. In Neuroscience Fourth Study Program, F. Schmitt and F. Worden, eds., pp. 1093-1109, MIT Press, Cambridge.
- Trotter, Y., Y. Fregnac, P. Buisseret (1981) Gating control of developmental plasticity by extraocular proprioception in kitten area 17. In Fourth European Conference on Visual Perception, Gouzieux, France.
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Huang, C., Yang, C., & Hsu, K. (2005) · Molecular Neurobiology · doi:10.1385/mn:32:3:223 · Cited by 46
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Mitchell, D. E., Gingras, G., & Kind, P. C. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.201392698 · Cited by 44
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Perrett, S. P., Dudek, S. M., Eagleman, D., Montague, P. R., & Friedlander, M. J. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-07-02308.2001 · Cited by 44
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Grossberg, S. (1988) · The MIT Press eBooks · doi:10.7551/mitpress/4934.003.0007 · Cited by 44
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Ramstead, M. J. D., Hesp, C., Tschantz, A., Smith, R., Constant, A., & Friston, K. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.11.024 · Cited by 43
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Lazari, A., Salvan, P., Cottaar, M., Papp, D. S., Rushworth, M. F. S., & Johansen‐Berg, H. (2021) · Cell Reports · doi:10.1016/j.celrep.2022.110951 · Cited by 47
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Cohen, E. J., Quarta, E., Bravi, R., Granato, A., & Minciacchi, D. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.12.048 · Cited by 45
Milo, V., Pedretti, G., Carboni, R., Calderoni, A., Ramaswamy, N., Ambrogio, S., & Ielmini, D. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2818978 · Cited by 44
Altinbilek, B., & Manahan‐Vaughan, D. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.07.045 · Cited by 43
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Nadal, J., & Parga, N. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1421 · Cited by 43
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Marshall, J. A. (1995) · Neural Networks · doi:10.1016/0893-6080(94)00099-8 · Cited by 41
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Cooper, E. A., & Norcia, A. M. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004268 · Cited by 40
Santschi, L. A., Reyes-Harde, M., & Stanton, P. K. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.82.3.1577 · Cited by 40
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Ragert, P., Camus, M., Vandermeeren, Y., Dimyan, M. A., & Cohen, L. G. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00274.2009 · Cited by 41
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Sadeh, S., Clopath, C., & Rotter, S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004307 · Cited by 35
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Self-citationLearning to see: genetic and environmental influences on visual development
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Cessac, B. (2012) · Cited by 38
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Galuske, R. A. W., Kim, D., Castrén, E., & Singer, W. (2000) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.2000.00213.x · Cited by 38
Zhang, X., & Poo, M. (2010) · Science China Life Sciences · doi:10.1007/s11427-010-0062-z · Cited by 38
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Scott, D. N., & Frank, M. J. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01374-6 · Cited by 40
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Bologna, M., Rocchi, L., Paparella, G., Nardella, A., Voti, P. L., Conte, A., Kojović, M., Rothwell, J. C., & Berardelli, A. (2015) · Brain stimulation · doi:10.1016/j.brs.2015.01.405 · Cited by 38
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Bekolay, T., Kolbeck, C., & Eliasmith, C. (2013) · eScholarship (California Digital Library) · Cited by 37
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Tieu, K. H., Keidel, A. L., McGann, J. P., Faulkner, B., & Brown, T. H. (1999) · Psychobiology · doi:10.3758/bf03332095 · Cited by 38
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Cheeran, B., Koch, G., Stagg, C. J., Baig, F., & Teo, J. (2010) · The Neuroscientist · doi:10.1177/1073858409349901 · Cited by 39
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Rao, R. P. N., & Ballard, D. H. (1998) · Network Computation in Neural Systems · doi:10.1088/0954-898x/9/2/005 · Cited by 37
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Castellani, G., Intrator, N., Shouval, H. Z., & Cooper, L. (1999) · Network Computation in Neural Systems · doi:10.1088/0954-898x/10/2/001 · Cited by 37
Self-citationBuonomano, D. V., Baxter, D. A., & Byrne, J. H. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90002-3 · Cited by 37
Intrator, N. (1993) · Neural Computation · doi:10.1162/neco.1993.5.3.443 · Cited by 37
Sensory deprivation without competition yields modest alterations of short-term synaptic dynamics
Finnerty, G. T., & Connors, B. W. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.230175697 · Cited by 37
Dynamics of functional connectivity in visual cortical networks: An overview
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Metadata
- Authors
- Bienenstock, E., Cooper, L., & Munro, P.
- Affiliations
- Université Paris-Sud (FR)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 1982-01-01
- Volume
- 2
- Issue
- 1
- Pages
- 32-48
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6564292
- OpenAlex
- W1733248925
- Cited by
- 2,896
- References
- 19
- Citations by year
- 2026: 71 · 2025: 91 · 2024: 98 · 2023: 108 · 2022: 102 · 2021: 104 · 2020: 112 · 2019: 93 · 2018: 112 · 2017: 108 · 2016: 87 · 2015: 117 · 2014: 118 · 2013: 119 · 2012: 112
- FWCI
- 4.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ittleson Foundation
- Brown University
- Fondation de France
- Office of Naval Research
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- orientation selectivity, binocular interaction, primary visual cortex, ocular dominance, synaptic plasticity, orientation tuning curves, synaptic efficacy, cortical development
- MeSH terms
- Animals, Cats, Dominance, Cerebral, Mathematics, Models, Neurological, Neurons, Orientation, Retina, Sensory Deprivation, Synapses, Visual Cortex, Visual Pathways, Visual Perception
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