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Summation and Division by Neurons in Primate Visual Cortex
Abstract
Recordings from monkey primary visual cortex (V1) were used to test a model for the visually driven responses of simple cells. According to the model, simple cells compute a linear sum of the responses of lateral geniculate nucleus (LGN) neurons. In addition, each simple cell's linear response is divided by the pooled activity of a large number of other simple cells. The cell membrane performs both operations; synaptic currents are summed and then divided by the total membrane conductance. Current and conductance are decoupled (by a complementary arrangement of excitation and inhibition) so that current depends only on the LGN inputs and conductance depends only on the cortical inputs. Closed form expressions were derived for fitting and interpreting physiological data. The model accurately predicted responses to drifting grating stimuli of various contrasts, orientations, and spatiotemporal frequencies.
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“In turn, this dynamic range can be adjusted through gain control that is mediated by the overall level of synaptic inhibition4,59,68,69.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [4]
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Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835
Self-citationNonlinear neural networks: Principles, mechanisms, and architectures
Grossberg, S. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90021-4 · Cited by 1,635
Striate cortex of monkey and cat: contrast response function.
Albrecht, D. G., & Hamilton, D. B. (1982) · Journal of Neurophysiology · doi:10.1152/jn.1982.48.1.217 · Cited by 953
Spatial summation in the receptive fields of simple cells in the cat's striate cortex.
Movshon, J. A., Thompson, I. D., & Tolhurst, D. J. (1978) · The Journal of Physiology · doi:10.1113/jphysiol.1978.sp012488 · Cited by 890
Synaptic background activity influences spatiotemporal integration in single pyramidal cells.
Bernander, Ö., Douglas, R. J., Martin, K. A. C., & Koch, C. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.24.11569 · Cited by 518
Bonds, A. B. (1989) · Visual Neuroscience · doi:10.1017/s0952523800004314 · Cited by 477
Motion selectivity and the contrast-response function of simple cells in the visual cortex
Albrecht, D. G., & Geisler, W. S. (1991) · Visual Neuroscience · doi:10.1017/s0952523800010336 · Cited by 433
The spatial selectivity of the visual cells of the cat
Campbell, F. W., Cooper, G. F., & Enroth‐Cugell, C. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008861 · Cited by 392
Visual Sensitivity And Parallel Retinocortical Channels
Shapley, R. M. (1990) · Annual Review of Psychology · doi:10.1146/annurev.ps.41.020190.003223 · Cited by 321
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Research works printed but not linked: 2
- GROSSBERG, S, NEURAL NETWORKS 1: 17 (1988).
- Koch, C., Synaptic Function: 637 (1987).
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Items 401–500 of 648
A neural habituation account of the negative compatibility effect.
Jacob, L. P. L., Potter, K. W., & Huber, D. E. (2021) · Journal of Experimental Psychology General · doi:10.1037/xge0001055 · Cited by 9
The stochastic properties of input spike trains control neuronal arithmetic
Bureš, Z. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0483-9 · Cited by 9
Modeling temporal response characteristics of V1 neurons with a dynamic normalization model
Mikaelian, S., & Simoncelli, E. P. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00529-x · Cited by 9
“whose driving current is provided by the ring of LGN a erents [1, 2].”
BackgroundEfficient Computation Based on Stochastic Spikes
Ernst, U. A., Rotermund, D., & Pawelzik, K. R. (2007) · Neural Computation · doi:10.1162/neco.2007.19.5.1313 · Cited by 9
“to receive and to average those spikes, and feed them back to the other populations as a divisive normalization, which is also a well-known neural operation that has been extensively studied in, for example, the early visual cortical areas (Heeger, 1992; Carandini & Heeger, 1994 ).”
BackgroundThe Emergence of Direction Selectivity in Cat Primary Visual Cortex
Humphrey, A. L., & SAUL, A. B. (2002) · Elsevier eBooks · doi:10.1016/b978-012552104-8/50010-9 · Cited by 9
Modelling the statistical processing of visual information
Šetić, M., Švegar, D., & Domijan, D. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.069 · Cited by 9
“Ratio computation is possible in a simple neuron model with divisive or shunting inhibition [ 3 ,12].”
BackgroundKalarickal, G. J., & Marshall, J. A. (2002) · Network Computation in Neural Systems · doi:10.1080/net.13.1.1.40 · Cited by 9
“Thus, activation levels are driven to remain within a bounded range, between −C and B. Other models have used divisive ( Carandini and Heeger 1994 ) or subtractive (Douglas et al 1995) inhibition to bound neuronal activation and stabilize network activity.”
BackgroundFast atomic decomposition by the inhibition method
Pece, A. E. C., & Petkov, N. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2000.903523 · Cited by 9
“[2]).”
A dynamic normalization model of temporal attentionPreprint
Denison, R. N., Carrasco, M., & Heeger, D. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.21.886051 · Cited by 9
Self-citationComparing experience- and description-based economic preferences across 11 countries
Anlló, H., Bavard, S., Benmarrakchi, F., Bonagura, D., Cerrotti, F., Cicue, M., Gueguen, M. C. M., Guzmán, E., Kadieva, D., Kobayashi, M., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01894-9 · Cited by 9
“According to this hypothesis, this feature can be understood as the result of fundamental neurocomputational constraints akin to those observable in perceptual decision-making 30–32 .”
Shi, J., Li, Q., & Wen, T. (2018) · Neuroscience Bulletin · doi:10.1007/s12264-018-0211-0 · Cited by 8
Ingram, T. G. J., King, J. L., & Crowder, N. A. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2019.00040 · Cited by 8
Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals
Mohl, J. T., Caruso, V. C., Tokdar, S. T., & Groh, J. M. (2020) · PubMed · doi:10.48550/arxiv.2001.11582 · Cited by 8
“This phenomenon, known as divisive normalization [2], has been observed in visual brain areas such as V1 andMT [3] as well as other sensory and cognitive domains [4, 5, 6, 7, 8, 9].”
BackgroundShapiro, J. T., Michaud, N. M., King, J. L., & Crowder, N. A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab269 · Cited by 8
Barton, B., & Brewer, A. A. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-407794-2.00005-5 · Cited by 8
Computing local edge probability in natural scenes from a population of oriented simple cells
Ramachandra, C. A., & Mel, B. W. (2013) · Journal of Vision · doi:10.1167/13.14.19 · Cited by 8
Prescott, S. A., & Koninck, Y. D. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_5 · Cited by 7
“Furthermore, the biophysical implementation of firing rate gain control, which plays an important computational role (Carandini and Heeger, 1994; Salinas and Thier, 2000) depends on the effects of background synaptic input.”
BackgroundZhang, S., Abbey, C. K., & Eckstein, M. P. (2009) · Visual Neuroscience · doi:10.1017/s0952523809090014 · Cited by 8
“It is possible that other nonlinear models’ fields (e.g., Carandini & Heeger, 1994; Geisler & Albrecht, 1997) not tested in this paper might be better at accounting for the human linear behavioral receptive.”
BackgroundComputational model for neural representation of multiple disparities
Watanabe, O., & Idesawa, M. (2003) · Neural Networks · doi:10.1016/s0893-6080(02)00186-7 · Cited by 8
“Recently, the normalization mechanism in visual area is investigated physiologically and theoretically (see Carandini & Heeger, 1994; van Wezel et al., 1996).”
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
Gür, B., Ramirez, L., Cornean, J., Thurn, F., Molina-Obando, S., Ramos-Traslosheros, G., & Silies, M. (2024) · Nature Communications · doi:10.1038/s41467-024-52724-5 · Cited by 8
“5a) 38,39 .”
Layher, G., Schrodt, F., Butz, M. V., & Heiko Neumann, B. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01287 · Cited by 9
“Activity normalization at the output stage is computed by a mechanism of shunting inhibition, like the non-linear divisive mechanisms proposed in Carandini and Heeger (1994); Carandini et al. (1999); Kouh and Poggio (2008); Carandini and Heeger (2012) (see Brosch and Neumann, 2014a for a formal…”
MethodRaffin, E., Witon, A., Salamanca-Giron, R., Huxlin, K. R., & Hummel, F. C. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab409 · Cited by 8
Shimegi, S., Ishikawa, A. W., Kida, H., Sakamoto, H., Hara, S.-I., & Sato, H. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00221.2012 · Cited by 7
Morita, K. (2009) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.012.2009 · Cited by 8
Miura, K., Sugita, Y., Matsuura, K., Inaba, N., Kawano, K., & Miles, F. A. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90535.2008 · Cited by 7
Constructing a metrics for blur perception with blur discrimination experiments
Chen, C., Chen, K.-P., Tseng, C., Kuo, S.-T., & Wu, K.-N. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.806107 · Cited by 7
Altered visual contrast gain control is sensitive for idiopathic generalized epilepsies
Won, D., Kim, W., Chaovalitwongse, W. A., & Tsai, J. J. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.12.008 · Cited by 7
A cortical locus for anisotropic overlay suppression of stimuli presented at fixation
Hansen, B. C., RICHARD, B., ANDRES, K., Johnson, A. P., Thompson, B. S., & Essock, E. A. (2015) · Visual Neuroscience · doi:10.1017/s0952523815000255 · Cited by 7
“3 , is similar to that of previous simulations of neuron behavior in the primary visual cortex (Heeger, 1992 ; Carandini & Heeger, 1994 ; Somers et al., 1995 ; Carandini & Ringach, 1997 ; Carandini et al., 1997 ; Troyer et al., 1998 ; Chance & Abbott, 2000 ; Pugh, Ringach, & Shapley, 2000 ; Troyer…”
MethodMicrostimulation in a spiking neural network model of the midbrain superior colliculus
Kasap, B., & Opstal, A. J. V. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006522 · Cited by 7
Greene, E. G., & Fiser, J. (1994) · Perception & Psychophysics · doi:10.3758/bf03206953 · Cited by 7
Zetzsche, C., & Krieger, G. (2001) · Elsevier eBooks · doi:10.1016/b978-012500451-0/50014-x · Cited by 7
Dynamic NormalizationPreprint
Heeger, D. J., & Zemlianova, K. O. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.22.002634 · Cited by 6
Self-citationHierarchical Models of the Visual System
Ricci, M. R., & Serre, T. (2020) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_345-2 · Cited by 8
Hierarchical Models of the Visual System
Ricci, M. R., & Serre, T. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_345 · Cited by 9
Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System
Abdul-Kreem, L. I., & Heiko Neumann, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142488 · Cited by 10
Contrast-Dependence of Temporal Frequency Tuning in Mouse V1
Camillo, D., Ahmadlou, M., & Heimel, J. A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00868 · Cited by 5
Local Interactions between Steady-State Visually Evoked Potentials at Nearby Flickering Frequencies
Liza, K., & Ray, S. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0180-22.2022 · Cited by 6
Domijan, D. (2011) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-011-0054-x · Cited by 6
“20, G inf describes the fixed strength of the distal dendritic impact on the trunk, and q represents a modulatory divisive inhibition that reduces the amount of activity in the trunk in proportion to the size of the object (Carandini & Heeger, 1994; Chance & Abbott, 2000).”
BackgroundMethodA Differential Model of the Complex Cell
Hansard, M. E., & Horaud, R. (2011) · Cited by 6
Luca, C. D., Gori, S., Mazzucchi, S., Dini, E., Cafalli, M., Siciliano, G., Papa, M., & Baldacci, F. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040769 · Cited by 6
“In vivo experiments on primate neurons of V1 and V2 areas showed that excitatory signals, responding to contrast gain, are counterbalanced by intracortical inhibitory feedbacks [19,20].”
BackgroundNeural adaptation in the auditory pathway of crickets and grasshoppers
Hildebrandt, K. J. (2010) · doi:10.18452/16150 · Cited by 6
“Both operations are known to be of central importance to the nervous system (Barlow and Levick, 1965; Carandini and Heeger, 1994; Peña and Konishi, 2001; Gabbiani et al., 2002), since they provide basic building blocks of computation: subtraction and division.”
Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signalsPreprint
Mohl, J. T., Caruso, V. C., Tokdar, S. T., & Groh, J. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/690958 · Cited by 6
“This phenomenon, known as divisive normalization [2], has been observed in visual brain areas such as V1 andMT [3] as well as other sensory and cognitive domains [4, 5, 6, 7, 8, 9].”
BackgroundDivisive normalization processors in the early visual system of the Drosophila brain
Lazar, A. A., & Zhou, Y. (2023) · Biological Cybernetics · doi:10.1007/s00422-023-00972-x · Cited by 4
“…evidence showing that divisive normalization may contribute to gain control in olfaction (Olsen et al. 2010), vertebrate retina (Beaudoin et al. 2007), primary visual cortex (Carandini and Heeger 1994), primary auditory cortex (Rabinowitz et al. 2011) and sensory integration (Ohshiro et al. 2017).”
Karmakar, S., & Sarkar, S. (2013) · Biological Cybernetics · doi:10.1007/s00422-013-0553-7 · Cited by 5
“The typical explanation for cross-orientation inhibition follows from divisive contrast gain control (Carandini and Heeger 1994; Beaudot and Mullen 2006).”
BackgroundSheliga, B. M., & FitzGibbon, E. J. (2023) · Journal of Vision · doi:10.1167/jov.23.10.11 · Cited by 5
A dynamic scale-mixture model of motion in natural scenes
Salisbury, J. M., & Palmer, S. E. (2025) · eLife · doi:10.7554/elife.104054 · Cited by 4
“Beyond single-cell adaptation, our results are also relevant to a population-level adaptation mechanisms known as divisive normalization [62, 63], in which neighboring neurons in a population are mutually inhibitory in a di-visive fashion.”
Local to global normalization dynamic by nonlinear local interactions
Keil, M. S. (2007) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2007.10.011 · Cited by 5
Mock, V. L., Luke, K. L., Hembrook‐Short, J. R., & Briggs, F. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00469.2018 · Cited by 5
“…response latencies were reduced by 14 ms, on average, in response to high-contrast compared with low-contrast stimuli, in line with previous reports (Albrecht 1995; Carandini and Heeger 1994; Dean and Tolhurst 1986; Kremers et al. 1997; Reid et al. 1992; Saul and Humphrey 1990; Sclar 1987).”
ResultIntegrating Anatomy and Physiology of the Primary Visual Pathway: From LGN to Cortex
Funke, K., Kisvárday, Z. F., Volgushev, M., & Wörgötter, F. (2002) · Physics of neural networks · doi:10.1007/978-0-387-21703-1_3 · Cited by 5
Change-based inference for invariant discrimination
Moazzezi, R., & Dayan, P. (2008) · Network Computation in Neural Systems · doi:10.1080/09548980802314917 · Cited by 5
“It is straightforward to calculate the centre of mass using divisive normalization (Carandini and Heeger 1994) in a feedforward network.”
MethodCamp, A. J., Cheong, S. K., Tailby, C., & Solomon, S. G. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00943.2010 · Cited by 5
“One biophysical implementation of this normalization model (Carandini and Heeger 1994; Carandini et al. 1997; Solomon and Lennie 2005) derives linked changes in response amplitude and phase from changes in membrane conductance.”
MethodDirection Selectivity and Spatiotemporal Separability in Simple Cortical Cells
García-Pérez, M. Á. (1999) · Journal of Computational Neuroscience · doi:10.1023/a:1008924122155 · Cited by 5
“Also, the model did not include a normalization stage based on shunting inhibition that was present in Heeger’s (1993) model (see also Carandini and Heeger, 1994; Carandini et al., 1997).”
MethodBINOCULAR VISUAL SENSATION IN READING A UNIFIED THEORY
Hussey, E. S. (2001) · Cited by 5
Kroell, L. M., & Rolfs, M. (2023) · eLife · doi:10.7554/elife.91236 · Cited by 5
Brain Rhythms Provide a Framework for Neural Syntax
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0006 · Cited by 3
Quantitative models of auditory cortical processing
Sadagopan, S., Kar, M., & Parida, S. (2023) · Hearing Research · doi:10.1016/j.heares.2023.108697 · Cited by 6
Parida, S., Liu, S. T., & Sadagopan, S. (2023) · Communications Biology · doi:10.1038/s42003-023-04816-z · Cited by 4
“Unlike the visual system, neither shunting inhibition by parvalbumin-expressing interneurons 35,42 nor contrast-dependent variability in membrane potential responses 43 seem to contribute to contrast gain control in the mouse auditory cortex 37 .”
Valuations of target items are drawn towards unavailable decoy items due to prior expectations
Izakson, L., Yoo, M., Hakim, A., Krajbich, I. M., Webb, R., & Levy, D. J. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae232 · Cited by 5
“One possible explanation for this effect is Divisive Normalization, a canonical neural computation that is also thought to underlie the influence of context on perception across a range of sensory modalities (19 – 22).”
BackgroundWade, A. R., & Baker, D. H. (2025) · Vision Research · doi:10.1016/j.visres.2025.108614 · Cited by 5
Steady-state measures of visual suppression
Baker, D. H., Vilidaitė, G., & Wade, A. R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009507 · Cited by 4
Conductance-based dendrites perform Bayes-optimal cue integration
Jordan, J., Sacramento, J., Wybo, W. A. M., Petrovici, M. A., & Senn, W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012047 · Cited by 7
Change-Based Inference in Attractor Nets: Linear Analysis
Moazzezi, R., & Dayan, P. (2010) · Neural Computation · doi:10.1162/neco_a_00051 · Cited by 4
“Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00051 by guest on 07 June 2021 Since the dynamics of the network in any case involve normalization (Carandini & Heeger, 1994), the second step is consider the actual input to the network to be a = a ′ qλ .”
BackgroundMethodInfluentialTheory of non-classical receptive field phenomena in the visual cortex
Ernst, U. A., Pawelzik, K. R., Wolf, F., & Geisel, T. (1999) · Neurocomputing · doi:10.1016/s0925-2312(99)00026-0 · Cited by 4
Illusory Bands in Orientation and Spatial Frequency: A Cortical Analog to Mach Bands
Essock, E. A., Hansen, B. C., & Haun, A. (2007) · Perception · doi:10.1068/p5695 · Cited by 4
“Also well documented is a mechanism that provides cortical contrast normalization in which a neuron's gain is adjusted by dividing its output (or input) by the pooled response of other cortical neurons (eg Carandini and Heeger 1994; Heeger 1992; Wilson and Humanski 1993).”
BackgroundA Hierarchical Model of Visual Rivalry
Dayan, P. (1996) · MPG.PuRe (Max Planck Society) · Cited by 4
Neural architectures for unifying brightness perception and image processing
Keil, M. S. (2002) · doi:10.18725/oparu-34 · Cited by 4
Genewein, T., & Braun, D. A. (2016) · Biological Cybernetics · doi:10.1007/s00422-016-0684-8 · Cited by 4
“It has been suggested as a normalization mechanism to regulate stimulus sensitivity in the invertebrate olfactory system [54], the mammalian retina [52], primary visual cortex [13,15] and other cortical areas [32].”
BackgroundSingle neuron local rational arithmetic revealed in phase space of input conductances
Wang, M., & Zhang, C. N. (1996) · Biophysical Journal · doi:10.1016/s0006-3495(96)79432-8 · Cited by 4
“As an example, we consider a normalization model for simple cells in the primate visual cortex (Heeger, 1992; Carandini and Heeger, 1994).”
BackgroundMethodInfluentialThe reconstitution of visual cortical feature selectivity in vitro
Schottdorf, M. (2018) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-6721 · Cited by 4
Multiple concurrent feedforward and feedback streams in a cortical hierarchyPreprint
Barzegaran, E., & Plomp, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.04.425277 · Cited by 4
Macé, M. J. ‐. (2006) · HAL · Cited by 4
Developmental trajectory of cortical somatostatin interneuron functionPreprint
Wang, A. Y., Ferguson, K., Gupta, J., Higley, M. J., & Cardin, J. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.05.583539 · Cited by 5
“Instead, SST-INs exerted an age-dependent increase in multiplicative modulation of PN responses, consistent with developmental emergence of an inhibitory circuit mechanism for normalization 54-56 .”
BackgroundNormalization in mouse primary visual cortex
Zayyad, Z., Maunsell, J. H. R., & MacLean, J. N. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295140 · Cited by 3
Unconditional stability of a recurrent neural circuit implementing divisive normalization
Rawat, S., Heeger, D. J., & Martiniani, S. (2024) · neural information processing systems · doi:10.52202/079017-0470 · Cited by 7
“It has been proposed that DN is achieved via a recurrent circuit [11,13,43–47].”
Self-citationKroell, L. M., & Rolfs, M. (2025) · eLife · doi:10.7554/elife.91236.4 · Cited by 3
Spatiotemporal Dynamics of Visual Perception Across Neural Maps and Pathways
Öğmen, H. (2005) · Springer eBooks · doi:10.1007/3-540-28247-5_1 · Cited by 3
“This shunting interaction transforms the input activity p1i for the ith element in the spatial frequency map into an output activity p2i = p1i/(A1+ ∑ i p1i), where A1 is the time constant of the response [12, 25].”
BackgroundMechanisms for Rapid Adaptive Control of Motion Processing in Macaque Visual Cortex
McLelland, D., Baker, P. M., Ahmed, B., Kohn, A., & Bair, W. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1418-11.2015 · Cited by 2
“When the mask is present with the target, it is more difficult to judge whether the mask is driving independent spikes because of the opposing influence of cross-orientation suppression (Morrone et al., 1982; Bonds, 1989; Carandini and Heeger, 1994).”
BackgroundResultInfluentialSchmitt, M. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.832810 · Cited by 3
Hayton, K., Moirogiannis, D., & Magnasco, M. O. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196566 · Cited by 3
Sheliga, B. M., Quaia, C., FitzGibbon, E. J., & Cumming, B. G. (2022) · Journal of Vision · doi:10.1167/jov.22.12.17 · Cited by 3
Wang, X., Nandy, A. S., & Jadi, M. P. (2023) · Scientific Reports · doi:10.1038/s41598-023-46722-8 · Cited by 4
The V1 Population Gains Normalization
Ganmor, E., Okun, M., & Lampl, I. (2009) · Neuron · doi:10.1016/j.neuron.2009.12.015 · Cited by 3
“…circuit of Sit et al. is 24, 2009 ª2009 Elsevier Inc. 779 based was proposed as a possible biophysically plausible implementation of a normalization operation (Carandini and Heeger, 1994), such as the one in Busse et al. What biophysical mechanisms may be responsible for divisive gain control?”
MethodDynamics of Nonlinear Feedback Control
Snippe, H. P., & van Hateren, J. H. (2007) · Neural Computation · doi:10.1162/neco.2007.19.5.1179 · Cited by 3
“It is well known that with increasing values of the input (e.g., luminance or contrast), neural systems not only decrease their gain but also adapt their temporal filtering (e.g., Sperling & Sondhi, 1968; Victor, 1987; Benardete, Kaplan, & Knight (1992); Carandini & Heeger, 1994; van Hateren, 2005).”
BackgroundTell me something interesting: Context dependent adaptation in somatosensory cortex
Davies, L. A., Garcia‐Lazaro, J. A., Schnupp, J. W. H., Wennekers, T., & Denham, S. L. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.12.003 · Cited by 3
Huggins, P. S., & Zucker, S. W. (2005) · Cited by 3
Lei, Q. (2015) · Cited by 3
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0001 · Cited by 4
Luminance Sensitivity and Contrast Detection
Kaplan, E. (2008) · Elsevier eBooks · doi:10.1016/b978-012370880-9.00294-2 · Cited by 3
Background of Visual Attention – Theory and Experiments
Zhang, L., & Lin, W. (2013) · Wiley eBooks · doi:10.1002/9780470828144.ch2 · Cited by 3
Toward a more biologically plausible model of object recognition
Kouh, M. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 3
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Computing stereoscopic disparity with binocular cortical simple and complex cells
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Iterative Orientation Tuning in V1: A Simple Cell Circuit with Cross-Orientation Suppression
Kolesnik, M., & Barlit, A. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45103-x_32 · Cited by 3
ORGaNICs: A Theory of Working Memory in Brains and MachinesPreprint
Heeger, D. J., & Mackey, W. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06288 · Cited by 4
“A push-pull arrangement of synaptic inputs can act like a current source (Carandini and Heeger 1994).”
BackgroundSelf-citationLeroux, C.-E., Fontvieille, C., & Bardin, F. (2025) · Vision Research · doi:10.1016/j.visres.2025.108652 · Cited by 2
Delayed integration of somatostatin interneurons into visual circuits
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“…inhibit local PNs. SST-INs exerted an age-dependent multiplicative modulation of PN responses, consistent with the developmental emergence of a key inhibitory circuit mechanism for normalization 70 – 72 where recurrent excitatory input to SST-INs promotes gain modulation in the local circuit 18 .”
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Cortical direction selectivity increases from the input to the output layers of visual cortex
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Bio-inspired visual mechanism lightweight network for edge detection
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“Studies [23] have shown that this modulation, especially in the primary visual cortex (V1), enhances the visual system’s ability to process complex stimuli.”
Stabilization of recurrent neural networks through divisive normalization
Morone, F., Rawat, S., Heeger, D. J., & Martiniani, S. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2601841123 · Cited by 6
“…computation, different biophysical mechanisms may perform normalization in different neural systems, including intracortical shunting inhibition [2, 9], thalamocortical synaptic depression [10], pre-synaptic inhibition [3], recurrent amplification (i.e., amplifying weak inputs more than strong…”
Self-citation
Metadata
- Authors
- Carandini, M., & Heeger, D. J.
- Affiliations
- New York University (US)
- Venue
- Science
- Published
- 1994-05-27
- Volume
- 264
- Issue
- 5163
- Pages
- 1333-1336
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2038718814
- Cited by
- 649
- References
- 28
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 22 · 2023: 19 · 2022: 22 · 2021: 23 · 2020: 18 · 2019: 30 · 2018: 12 · 2017: 10 · 2016: 17 · 2015: 22 · 2014: 20 · 2013: 20 · 2012: 31
- FWCI
- 9.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institutes of Health (5r29mh050228-05)
- National Institute of Mental Health (1-R29-MH-50228-01)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- primary visual cortex, simple cells, lateral geniculate nucleus, synaptic summation, membrane conductance, excitation-inhibition balance, contrast sensitivity, orientation selectivity, monkey visual cortex
- MeSH terms
- Animals, Electric Conductivity, Geniculate Bodies, Macaca, Membrane Potentials, Models, Neurological, Neurons, Synapses, Visual Cortex
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