The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“If this were the case, it has been argued that the system would be unstable and quickly saturate (366–371).”
From Synaptic memory and CaMKII (2023) · cites this paper as [367]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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.
Cited by
Sorted by citations
Items 801–900 of 1,006
Stanton, P. (1995) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.92.5.1724 · Cited by 58
Self-Organized Synaptic Plasticity Contributes to the Shaping of γ and β OscillationsIn Vitro
Bibbig, A., Faulkner, H. J., Whittington, M. A., & Traub, R. D. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-22-09053.2001 · Cited by 62
Transcranial Direct Current Stimulation Based Metaplasticity Protocols in Working Memory
Carvalho, S., Boggio, P. S., Gonçalves, Ó. F., Vigário, A. R., Faria, M. J. M., Silva, S. M., do Rêgo, G. G., Fregni, F., & Leite, J. (2014) · Brain stimulation · doi:10.1016/j.brs.2014.11.011 · Cited by 52
A neuromorphic VLSI design for spike timing and rate based synaptic plasticity
Azghadi, M. R., Al-Sarawi, S. F., Abbott, D., & Iannella, N. L. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.03.003 · Cited by 52
Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation
de Brito, C. S. N., & Wulfram Gerstner, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005070 · Cited by 54
Protocols of non-invasive brain stimulation for neuroplasticity induction
He, W., Fong, P., Leung, T. W. H., & Huang, Y. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.02.045 · Cited by 52
Increasing stimulus similarity drives nonmonotonic representational change in hippocampus
Wammes, J. D., Norman, K. A., & Turk‐Browne, N. B. (2022) · eLife · doi:10.7554/elife.68344 · Cited by 55
Chapter 14 Nmda-receptor-dependent synaptic plasticity in the visual cortex
Bear, M. F. (1996) · Progress in brain research · doi:10.1016/s0079-6123(08)62541-8 · Cited by 53
Neural Correlates of Learning in the Prefrontal Cortex of the Monkey: A Predictive Model
Guigon, E., Dorizzi, B., Burnod, Y., & Schultz, W. (1995) · Cerebral Cortex · doi:10.1093/cercor/5.2.135 · Cited by 52
Fregni, F., Boggio, P. S., Valle, A. C., Otachi, P. T. M., Thut, G., Rigonatti, S. P., Marcolin, M. A., Fecteau, S., Pascual‐Leone, Á., Fiore, L. A., et al. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.02.015 · Cited by 52
The Learning Barrier: Moving from Innate to Learned Systems of Communication
Oliphant, M. (1999) · Adaptive Behavior · doi:10.1177/105971239900700309 · Cited by 52
Plasticity in auditory cortical circuitry
Ahissar, E., & Ahissar, M. (1994) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(94)90060-4 · Cited by 52
Nelson, P. A., Sage, J. R., Wood, S. C., Davenport, C. M., Anagnostaras, S. G., & Boulanger, L. M. (2013) · Learning & Memory · doi:10.1101/lm.031351.113 · Cited by 49
Rate and pulse based plasticity governed by local synaptic state variables
Mayr, C. (2010) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2010.00033 · Cited by 51
Guo, J., Liu, Y., Li, Y., Li, F., & Huang, F. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c14325 · Cited by 52
Yu, Y., Migliore, M., Hines, M. L., & Shepherd, G. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1834-14.2014 · Cited by 51
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?
Knoblauch, A., Häuser, F., Gewaltig, M.-O., Körner, E., & Palm, G. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00055 · Cited by 56
High frequency somatosensory stimulation in dystonia: Evidence fordefective inhibitory plasticity
Erro, R., Rocchi, L., Antelmi, E., Liguori, R., Tinazzi, M., Berardelli, A., Rothwell, J. C., & Bhatia, K. P. (2018) · Movement Disorders · doi:10.1002/mds.27470 · Cited by 51
Toyoizumi, T., Pfister, J.-P., Aihara, K., & Wulfram Gerstner, B. (2007) · Neural Computation · doi:10.1162/neco.2007.19.3.639 · Cited by 49
Regulation of synaptic plasticity in memory and memory decline with aging
Foster, T. C. (2002) · Progress in brain research · doi:10.1016/s0079-6123(02)38083-x · Cited by 51
Feature-based classification of myoelectric signals using artificial neural networks
Gallant, P. J., Morin, E. L., & Peppard, L. (1998) · Medical & Biological Engineering & Computing · doi:10.1007/bf02523219 · Cited by 51
A model of ocular dominance column development by competition for trophic factor
Harris, A. E., Ermentrout, B., & Small, S. L. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.18.9944 · Cited by 51
N-methyl-D-aspartate-evoked calcium uptake by kitten visual cortex maintained in vitro
Feldman, D. E., Sherin, J., Press, W., & Bear, M. F. (1990) · Experimental Brain Research · doi:10.1007/bf00228153 · Cited by 51
Tsanov, M., & Manahan-Vaughan, D. (2008) · The Neuroscientist · doi:10.1177/1073858408315655 · Cited by 65
Short-term and long-term plasticity interaction in human primary motor cortex
Iezzi, E., Suppa, A., Conte, A., Voti, P. L., Bologna, M., & Berardelli, A. (2011) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2011.07674.x · Cited by 50
Rosenkranz, K., Seibel, J., Kačar, A., & Rothwell, J. C. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5139-13.2014 · Cited by 55
Building a mechanistic model of the development and function of the primary visual cortex
Bednar, J. A. (2012) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2011.12.001 · Cited by 50
Jedlička, P., Beňušková, Ľ., & Abraham, W. C. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004588 · Cited by 50
Abnormal cortical and brain stem plasticity in Gilles de la Tourette syndrome
Suppa, A., Belvisi, D., Bologna, M., Marsili, L., Berardelli, I., Moretti, G., Pasquini, M., Fabbrini, G., & Berardelli, A. (2011) · Movement Disorders · doi:10.1002/mds.23706 · Cited by 52
Towards a General Theory of Neural Computation Based on Prediction by Single Neurons
Fiorillo, C. D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003298 · Cited by 50
Kouvaros, S., & Papatheodoropoulos, C. (2016) · Hippocampus · doi:10.1002/hipo.22655 · Cited by 54
Limits on lability: boundaries of reconsolidation and the relationship to metaplasticity
Zhang, J. J., Haubrich, J., Bernabo, M., Finnie, P. S. B., & Nader, K. (2018) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2018.02.018 · Cited by 50
Kervern, M., Angeli, A., Nicole, O., Léveillé, F., Parent, B., Villette, V., Buisson, A., & Dutar, P. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-120394 · Cited by 59
Synaptic plasticity, metaplasticity and bcm theory
Jedlička, P. (2002) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.1007/bf03656535 · Cited by 49
Tremere, L. A., Hicks, T. P., & Rasmusson, D. D. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.1.94 · Cited by 50
Targeting metaplasticity mechanisms to promote sustained antidepressant actions
Brown, K. A., & Gould, T. D. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02397-1 · Cited by 49
A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits
Walters, B., Jacob, M. V., Amirsoleimani, A., & Azghadi, M. R. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300136 · Cited by 49
Abnormal experimentally- and behaviorally-induced LTP-like plasticity in focal hand dystonia
Belvisi, D., Suppa, A., Marsili, L., Stasio, F. D., Parvez, A. K., Agostino, R., Fabbrini, G., & Berardelli, A. (2012) · Experimental Neurology · doi:10.1016/j.expneurol.2012.11.003 · Cited by 49
Calcium threshold shift enables frequency-independent control of plasticity by an instructive signal
Piochon, C., Titley, H. K., Simmons, D. H., Grasselli, G., Elgersma, Y., & Hansel, C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613897113 · Cited by 49
Plasticity and Adaptation in Neuromorphic Biohybrid Systems
George, R., Chiappalone, M., Giugliano, M., Levi, T., Vassanelli, S., Partzsch, J., & Mayr, C. (2020) · iScience · doi:10.1016/j.isci.2020.101589 · Cited by 56
Deep Predictive Learning in Neocortex and Pulvinar
O’Reilly, R. C., Russin, J., Zolfaghar, M., & Rohrlich, J. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01708 · Cited by 49
Evolution of Motor Memory During the Seconds After Observation of Motor Error
Huang, V. S., & Shadmehr, R. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01281.2006 · Cited by 51
Beyond spike timing: the role of nonlinear plasticity and unreliable synapses
Senn, W. (2002) · Biological Cybernetics · doi:10.1007/s00422-002-0350-1 · Cited by 49
Buchs, N. J., & Senn, W. (2002) · Journal of Computational Neuroscience · doi:10.1023/a:1020210230751 · Cited by 49
Obata, S. (1999) · Cerebral Cortex · doi:10.1093/cercor/9.3.238 · Cited by 50
Blais, B. S., Shouval, H. Z., & Cooper, L. N. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.3.1083 · Cited by 49
Self-citationAnti-Hebbian learning in topologically constrained linear networks: a tutorial
Palmieri, F., Zhu, J., & Chang, C. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.248453 · Cited by 49
Stable Learning in Stochastic Network States
El‐Boustani, S., Yger, P., Frégnac, Y., & Alain Destexhe, A. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2496-11.2012 · Cited by 50
The Immediate Early Gene Arc Is Not Required for Hippocampal Long-Term Potentiation
Kyrke‐Smith, M., Volk, L. J., Cooke, S. F., Bear, M. F., Huganir, R. L., & Shepherd, J. D. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0008-20.2021 · Cited by 49
Chipman, P. H., Fung, C. C. A., Fernández, A. P., Sawant, A., Tedoldi, A., Kawai, A., Gautam, S. G., Kurosawa, M., Abe, M., Sakimura, K., et al. (2021) · eLife · doi:10.7554/elife.70818 · Cited by 55
Chapter 1 Theoretical Models of Neural Circuit Development
Simpson, H. D., Mortimer, D., & Goodhill, G. J. (2009) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/s0070-2153(09)01201-0 · Cited by 48
Buschler, A., Goh, J. J., & Manahan‐Vaughan, D. (2012) · Hippocampus · doi:10.1002/hipo.22041 · Cited by 49
Hardingham, N. R., Wright, N. F., Dachtler, J., & Fox, K. D. (2008) · Neuron · doi:10.1016/j.neuron.2008.10.018 · Cited by 46
Homeostatic Metaplasticity in the Human Somatosensory Cortex
Bliem, B., Müller‐Dahlhaus, J. F. M., Dinse, H. R., & Ziemann, U. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20106 · Cited by 48
Suppa, A., Biasiotta, A., Belvisi, D., Marsili, L., Cesa, S. L., Truini, A., Cruccu, G., & Berardelli, A. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs182 · Cited by 51
How Many Types of Dystonia? Pathophysiological Considerations
Quartarone, A., & Ruge, D. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00012 · Cited by 48
Tognini, P., Manno, I., Bonaccorsi, J., Cenni, M. C., Sale, A., & Maffei, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034815 · Cited by 49
Levine, D. S. (2002) · Wiley eBooks · doi:10.1002/0471214426.pas0406 · Cited by 48
Stoneham, E. T., Sanders, E. M., Sanyal, M., & Dumas, T. C. (2010) · Biological Bulletin · doi:10.1086/bblv219n2p81 · Cited by 49
Oberman, L. M., Ifert‐Miller, F., Najib, U., Bashir, S., Gonzalez–Heydrich, J., Picker, J. D., Rotenberg, A., & Pascual‐Leone, Á. (2016) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2015.0166 · Cited by 50
Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity
Demin, V. A., & Nekhaev, D. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00079 · Cited by 50
A Network Perspective on Sensorimotor Learning
Sohn, H., Meirhaeghe, N., Rajalingham, R., & Jazayeri, M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.11.007 · Cited by 45
Rema, V., & Ebner, F. F. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-32-10378.2003 · Cited by 47
Kimura, R., & Matsuki, N. (2008) · The Journal of Physiology · doi:10.1113/jphysiol.2008.151894 · Cited by 46
What can the hippocampal representation of environmental geometry tell us about Hebbian learning?
Lever, C., Burgess, N., Cacucci, F., Hartley, T., & O'Keefe, J. M. (2002) · Biological Cybernetics · doi:10.1007/s00422-002-0360-z · Cited by 52
Segregation of on and off Retinogeniculate Connectivity Directed by Patterned Spontaneous Activity
Lee, C. W., Eglen, S. J., & Wong, R. (2002) · Journal of Neurophysiology · doi:10.1152/jn.00372.2002 · Cited by 47
Dendritic calcium signaling in cerebellar Purkinje cell
Kitamura, K., & Kano, M. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.08.001 · Cited by 45
A learning rule for extracting spatio-temporal invariances
Stone, J. V., & Bray, A. J. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/3/008 · Cited by 47
Structured Long-Range Connections Can Provide a Scaffold for Orientation Maps
Shouval, H. Z., Goldberg, D. H., Jones, J. P., Beckerman, M., & Cooper, L. N. (2000) · Journal of Neuroscience · doi:10.1523/jneurosci.20-03-01119.2000 · Cited by 47
Self-citationA model of prenatal acquisition of speech parameters.
Seebach, B. S., Intrator, N., LIEBERMAN, P. H., & Cooper, L. N. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.16.7473 · Cited by 47
Self-citationCompetition for Neurotrophic Factors: Ocular Dominance Columns
Elliott, T., & Shadbolt, N. R. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-15-05850.1998 · Cited by 46
Modulating the granularity of category formation by global cortical states
Kim, Y. (2008) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.001.2008 · Cited by 47
Unconjugated Bilirubin Exposure Impairs Hippocampal Long-Term Synaptic Plasticity
Chang, F.-Y., Lee, C.-C., Huang, C., & Hsu, K. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005876 · Cited by 46
Altered Synaptic Plasticity in Tourette's Syndrome and Its Relationship to Motor Skill Learning
Brandt, V. C., Nießen, E., Ganos, C., Kahl, U., Bäumer, T., & Münchau, A. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098417 · Cited by 50
Li, J., & Baccei, M. L. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3547-15.2016 · Cited by 46
Sergiou, C. S., Santarnecchi, E., Romanella, S. M., Wieser, M. J., Franken, I. H. A., Rassin, E., & van Dongen, J. D. M. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.05.007 · Cited by 48
Zeuner, K. E., & Molloy, F. M. (2008) · Neurorehabilitation · doi:10.3233/nre-2008-23105 · Cited by 46
Homeostatic shutdown of long-term potentiation in the adult hippocampus
Roth-Alpermann, C., Morris, R., Körte, M., & Bonhoeffer, T. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600894103 · Cited by 46
Elliott, T., & Shadbolt, N. R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-18-07951.1999 · Cited by 47
Learning and Memory: Basic Mechanisms
Brown, T. H., Byrne, J. H., LaBar, K. S., LeDoux, J. E., Lindquist, D. H., Thompson, R. F., & Teyler, T. J. (2004) · Elsevier eBooks · doi:10.1016/b978-012148660-0/50019-6 · Cited by 46
Olson, S. J., & Grossberg, S. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00003-3 · Cited by 46
Complexities and uncertainties of neuronal network function
Parker, D. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1779 · Cited by 47
The K-complex and slow oscillation in terms of a mean-field cortical model
Wilson, M. T., Steyn‐Ross, D. A., SLEIGH, J. W., Steyn‐Ross, M. L., Wilcocks, L. C., & Gillies, I. P. (2006) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-7948-6 · Cited by 45
Miikkulainen, R., Bednar, J. A., Choe, Y., & Sirosh, J. (1997) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(08)60286-8 · Cited by 49
Soft self-healing resistive-based sensors inspired by sensory transduction in biological systems
Georgopoulou, A., Brancart, J., Terryn, S., Bosman, A. W., Norvez, S., Assche, G. V., Fumiya Iida, A., Vanderborght, B., & Clemens, F. J. (2022) · Applied Materials Today · doi:10.1016/j.apmt.2022.101638 · Cited by 45
Age-related dysregulation of homeostatic control in neuronal microcircuits
Radulescu, C. I., Doostdar, N., Zabouri, N., Melgosa-Ecenarro, L., Wang, X., Sadeh, S., Pavlidi, P., Airey, J., Kopanitsa, M. V., Clopath, C., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01451-z · Cited by 53
Wang, X., Chen, G., Gao, W., & Ebner, T. J. (2009) · Neuroscience · doi:10.1016/j.neuroscience.2009.01.071 · Cited by 47
Modulation of executive control in dual tasks with transcranial direct current stimulation (tDCS)
Strobach, T., Soutschek, A., Antonenko, D., Flöel, A., & Schubert, T. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.12.024 · Cited by 50
Colitti-Klausnitzer, J., Kulla, A., & Manahan‐Vaughan, D. (2003) · Neuropharmacology · doi:10.1016/j.neuropharm.2003.09.019 · Cited by 45
Short-term depression and long-term plasticity together tune sensitive range of synaptic plasticity
Deperrois, N., & Graupner, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008265 · Cited by 46
Dopaminergic Control of Long-Term Depression/Long-Term Potentiation Threshold in Prefrontal Cortex
Sheynikhovich, D., Otani, S., & Arleo, A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0466-13.2013 · Cited by 45
“Heterosynaptic” LTD in the Dentate Gyrus of Anesthetized Rat Requires Homosynaptic Activity
Abraham, W. C., Logan, B. J., Wolff, A. R., & Beňušková, Ľ. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00250.2007 · Cited by 45
Analogous Synaptic Plasticity Profiles Emerge from Disparate Channel Combinations
Anirudhan, A., & Narayanan, R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4223-14.2015 · Cited by 47
Beňušková, Ľ., Diamond, M. E., & Ebner, F. F. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.11.4791 · Cited by 45
Grossberg, S., & Marshall, J. A. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90013-0 · Cited by 45
Grüner, U., Eggers, C., Ameli, M., Sarfeld, A., Fink, G. R., & Nowak, D. A. (2009) · Journal of Neural Transmission · doi:10.1007/s00702-009-0356-0 · Cited by 44
Priming the Brain to Capitalize on Metaplasticity in Stroke Rehabilitation
Cassidy, J. M., Gillick, B. T., & Carey, J. R. (2013) · Physical Therapy · doi:10.2522/ptj.20130027 · Cited by 46
Sczesny-Kaiser, M., Beckhaus, K., Dinse, H. R., Schwenkreis, P., Tegenthoff, M., & Höffken, O. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00116 · Cited by 44
Chang, J. T.-H., Whitney, D. E., & Fitzpatrick, D. (2020) · Neuron · doi:10.1016/j.neuron.2020.04.022 · Cited by 44
Xiong, W., Wei, H., Xiang, X., Cao, J., Dong, Z., Wang, Y., Xu, T., & Xu, L. (2004) · Brain Research · doi:10.1016/j.brainres.2004.01.051 · Cited by 44
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review