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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).”
From Synaptic memory and CaMKII (2023) · cites this paper as [367]
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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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Garcia, R. (2001) · Synapse · doi:10.1002/syn.1040 · Cited by 99
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Upadhyay, N. K., Joshi, S., & Yang, J. J. (2016) · Science China Information Sciences · doi:10.1007/s11432-016-5565-1 · Cited by 97
Mockett, B. G., Coussens, C. M., & Abraham, W. C. (2002) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.2002.02008.x · Cited by 96
Fujii, H., Inoue, M., Okuno, H., Sano, Y., Takemoto‐Kimura, S., Kitamura, K., Kano, M., & Bito, H. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.03.033 · Cited by 94
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Krucker, T., Siggins, G. R., McNamara, R. K., Lindsley, K. A., Dao, A. T. T., Allison, D. W., de Lecea, L., Lovenberg, T. W., Sutcliffe, J. G., & Gerendasy, D. D. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-13-05525.2002 · Cited by 97
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Franks, K. M., & B, T. J. S. A. (2002) · BioEssays · doi:10.1002/bies.10193 · Cited by 98
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Fertonani, A., Brambilla, M., Cotelli, M., & Miniussi, C. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00131 · Cited by 93
Amadi, U., Allman, C., Johansen‐Berg, H., & Stagg, C. J. (2015) · Brain stimulation · doi:10.1016/j.brs.2015.04.010 · Cited by 93
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Rebesco, J. M. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00039 · Cited by 94
Castel-Lacanal, E., Marqué, P., Tardy, J., Boissezon, X. D., Guiraud, V., Chollet, F., Loubinoux, I., & Simonetta‐Moreau, M. (2008) · Neurorehabilitation and neural repair · doi:10.1177/1545968308322841 · Cited by 92
Pittà, M. D., Volman, V., Berry, H., Parpura, V., Volterra, A., & Ben‐Jacob, E. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00098 · Cited by 96
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Hassanzahraee, M., Nitsche, M. A., Zoghi, M., & Jaberzadeh, S. J. (2020) · Brain stimulation · doi:10.1016/j.brs.2020.02.027 · Cited by 92
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Dong, D. W., & Hopfield, J. J. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x_3_3_002 · Cited by 89
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Corticosterone shifts different forms of synaptic potentiation in opposite directions
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Primed Facilitation of Homosynaptic Long-Term Depression and Depotentiation in Rat Hippocampus
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Todd, G., Flavel, S. C., & Ridding, M. C. (2009) · Experimental Brain Research · doi:10.1007/s00221-009-1791-8 · Cited by 86
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Savin, C., Joshi, P., & Triesch, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000757 · Cited by 88
Law, C.-T., & Cooper, L. N. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.16.7797 · Cited by 84
Self-citationLebedev, M. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.1.23 · Cited by 86
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Nakao, K., Matsuyama, K., Matsuki, N., & Ikegaya, Y. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0405709101 · Cited by 86
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Luczak, A., McNaughton, B. L., & Kubo, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00430-y · Cited by 86
Philpot, B. D., Weisberg, M. P., Ramos, M. S., Sawtell, N. B., Tang, Y., Tsien, J. Z., & Bear, M. F. (2001) · Neuropharmacology · doi:10.1016/s0028-3908(01)00136-8 · Cited by 85
Erwin, E., & Miller, K. D. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-23-09870.1998 · Cited by 83
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Éthier, C., Gallego, J. Á., & Miller, L. E. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.03.007 · Cited by 84
Time scales of memory, learning, and plasticity
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Neural plasticity of mushroom body-extrinsic neurons in the honeybee brain
Menzel, R., & Manz, G. (2005) · Journal of Experimental Biology · doi:10.1242/jeb.01908 · Cited by 88
Hardt, O. M., Nader, K., & Wang, Y.-T. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0141 · Cited by 89
Wang, D.-J., Su, L.-D., Wang, Y.-N., Yang, D., Sun, C.-L., Zhou, L., Wang, X.-X., & Shen, Y. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4064-13.2014 · Cited by 76
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Homeostatic Plasticity and External Input Shape Neural Network Dynamics
Zierenberg, J., Wilting, J., & Priesemann, V. (2018) · Physical Review X · doi:10.1103/physrevx.8.031018 · Cited by 80
Optimising synaptic learning rules in linear associative memories
Dayan, P., & Willshaw, D. J. (1991) · Biological Cybernetics · doi:10.1007/bf00206223 · Cited by 83
Critical Period for Inhibitory Plasticity in RodentBinocular V1
Maffei, A., Lambo, M. E., & Turrigiano, G. G. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5340-09.2010 · Cited by 82
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Embodied inference: or "I think therefore I am, if I am what I think"
Friston, K. J. (2011) · Cited by 84
Biophysical model of synaptic plasticity dynamics
Abarbanel, H. D. I., Gibb, L., Huerta, R., & Rabinovich, M. I. (2003) · Biological Cybernetics · doi:10.1007/s00422-003-0422-x · Cited by 81
Emerging memory technologies for neuromorphic computing
Kim, C.-H., Lim, S., Woo, S. Y., Kang, W.-M., Seo, Y.-T., Lee, S., Lee, S., Kwon, D., Oh, S., Noh, Y., et al. (2018) · Nanotechnology · doi:10.1088/1361-6528/aae975 · Cited by 83
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Kojić, L., Gu, Q., Douglas, R. M., & Cynader, M. S. (1997) · Developmental Brain Research · doi:10.1016/s0165-3806(97)00083-7 · Cited by 82
Causal networks in simulated neural systems
Seth, A. K. (2007) · Cognitive Neurodynamics · doi:10.1007/s11571-007-9031-z · Cited by 83
Gold, J. I., & Bear, M. F. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.9.3941 · Cited by 84
Correlated binocular activity guides recovery from monocular deprivation
Kind, P. C., Mitchell, D. E., Ahmed, B., Blakemore, C. B., Bonhoeffer, T., & Sengpiel, F. (2002) · Nature · doi:10.1038/416430a · Cited by 82
Outline of a theory for the ontogenesis of iso-orientation domains in visual cortex
von der Malsburg, C., & Cowan, J. D. (1982) · Biological Cybernetics · doi:10.1007/bf00387213 · Cited by 82
A Learning Rule for the Emergence of Stable Dynamics and Timing in Recurrent Networks
Buonomano, D. V. (2005) · Journal of Neurophysiology · doi:10.1152/jn.01250.2004 · Cited by 83
Aquaporin-4 water channels and synaptic plasticity in the hippocampus
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Stone, J. V. (1996) · Neural Computation · doi:10.1162/neco.1996.8.7.1463 · Cited by 81
Hazime, F. A., Baptista, A. F., de Freitas, D. G., Monteiro, R. L., Maretto, R. L., Hasue, R. H., & João, S. M. A. (2017) · European Journal of Pain · doi:10.1002/ejp.1037 · Cited by 80
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Bukalo, O., Schachner, M., & Dityatev, A. E. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1022-07.2007 · Cited by 80
Brain aerobic glycolysis and motor adaptation learning
Shannon, B. J., Vaishnavi, S., Vlassenko, A. G., Shimony, J. S., Rutlin, J. R., & Raichle, M. E. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604977113 · Cited by 86
A metaplasticity view of the interaction between homeostatic and Hebbian plasticity
Yee, A. X., Hsu, Y., & Chen, L. (2017) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0155 · Cited by 85
Chen, Y.-L., Huang, C., & Hsu, K. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-11-03705.2001 · Cited by 79
Doyère, V., Srebro, B., & Laroche, S. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.2.571 · Cited by 80
López, A. J., Kramár, E. A., Matheos, D. P., White, A. O., Kwapis, J. L., Ciernia, A. V., Sakata, K., Espinoza, M. E., & Wood, M. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3682-15.2016 · Cited by 80
Self-Organization Toward Criticality by Synaptic Plasticity
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Dendritic Spine Dynamics Regulate the Long-Term Stability of Synaptic Plasticity
O’Donnell, C., Nolan, M. F., & van Rossum, M. C. W. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2520-11.2011 · Cited by 78
17β-estradiol suppresses expression of long-term depression in aged rats
Vouimba, R., Foy, M. R., Foy, J. G., & Thompson, R. F. (2000) · Brain Research Bulletin · doi:10.1016/s0361-9230(00)00377-4 · Cited by 76
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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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