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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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Items 201–300 of 648
Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images
Malo, J., & Laparra, V. (2010) · Neural Computation · doi:10.1162/neco_a_00046 · Cited by 48
The atoms of vision: Cartesian or polar?
Zetzsche, C., Krieger, G., & Wegmann, B. (1999) · Journal of the Optical Society of America A · doi:10.1364/josaa.16.001554 · Cited by 51
Ulrich, D. (2003) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.2003.02942.x · Cited by 48
Selective Bayes: Attentional load and crowding
Dayan, P., & Solomon, J. A. (2010) · Vision Research · doi:10.1016/j.visres.2010.04.014 · Cited by 51
Contextual modulation involves suppression and facilitation from the center and the surround
Meese, T. S., Summers, R. J., Holmes, D. J., & Wallis, S. A. (2007) · Journal of Vision · doi:10.1167/7.4.7 · Cited by 49
“In particular, they have implications for contrast gain control (Carandini & Heeger, 1994; Foley, doi: 10 .1167 /7 .4 .7 Received July 17, 2006; published March 22, 2007 ISSN 1534-7362 * ARVO Downloaded From: https://jov.arvojournals.org/pdfaccess.ashx?url=/data/journals/jov/933521/ on…”
MethodDivisive inhibition in recurrent networks
Chance, F. S., & Abbott, L. F. (2000) · Network Computation in Neural Systems · doi:10.1088/0954-898x_11_2_301 · Cited by 49
“Inhibition that acts divisively, rather than subtractively, has been implemented in feedforward models of primary visual cortex to account for a number of response features (Heeger 1992, 1993, Carandini and Heeger 1994).”
BackgroundCoding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram
Naud, R., & Wulfram Gerstner, B. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002711 · Cited by 53
“The properties of the QR or EME1 decoder can be used to find biophysical correlates of neural decoding such as previously proposed for short term plasticity [71,72], non-linear dendrites [73] or lateral inhibition [74].”
BackgroundBadia, S. B. I., Bernardet, U., & Verschure, P. F. M. J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000701 · Cited by 49
“of cortical neurons [3,4], while several other studies have”
ResultChang, M. H., Armstrong, K. M., & Moore, T. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2967-11.2012 · Cited by 44
“One candidate mechanism underlying the stimulus-driven drop in FF is shunting inhibition, which can modulate firing rates of neighboring neurons through lateral connections (Mitchell and Silver, 2003) and has been proposed as a general cortical mechanism for response normalization (Carandini and Heeger, 1994; Reynolds…”
BackgroundAn image-computable model for the stimulus selectivity of gamma oscillations
Hermes, D., Petridou, N., Kay, K., & Winawer, J. A. (2019) · eLife · doi:10.7554/elife.47035 · Cited by 50
Fernandez, F. R., Broicher, T., Truong, A., & White, J. A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5076-10.2011 · Cited by 44
“Although simple, the tuning and form of this relationship is critical for processing of stimuli and network dynamics (Carandini and Heeger, 1994; Treue and Martínez Trujillo, 1999; Priebe and Ferster, 2008).”
MethodContrast gain control and cortical TrkB signaling shape visual acuity
Heimel, J. A., Saiepour, M. H., Chakravarthy, S., Hermans, J. M., & Levelt, C. N. (2010) · Nature Neuroscience · doi:10.1038/nn.2534 · Cited by 44
Neural basis of visual selective attention
Chelazzi, L., Libera, C. D., Sani, I., & Santandrea, E. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.117 · Cited by 45
The origin of the oblique effect examined with pattern adaptation and masking
McMahon, M. J., & MacLeod, D. I. A. (2003) · Journal of Vision · doi:10.1167/3.3.4 · Cited by 46
“Renewed interest in contrast gain control sparked a new class of models (Foley & Chen, 1997; Watson & Solomon, 1997), which normalize the linear response of each unit by a measure of stimulus energy from a large pool of neurons (Carandini & Heeger, 1994; Geisler & Albrecht, 1992; Heeger, 1992).”
Soto, F. A., & Wasserman, E. A. (2011) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-011-0070-x · Cited by 45
“Second, the two main operations deployed by the model (see below) can be implemented by biologically plausible neural microcircuits (Carandini & Heeger, 1994; Kouh & Poggio, 2008; Yu, Giese, & Poggio, 2002); evidence suggests that these operations are used in the visual cortex (Gawne & Martin,…”
MethodPredicting neuronal dynamics with a delayed gain control model
Zhou, J., Benson, N. C., Kay, K., & Winawer, J. A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007484 · Cited by 45
“On the other hand, within the RC-circuit model class, Carandini and Heeger 1994 [10], Mikaelian and Simoncelli 2001 [12], and Mante et al 2008 [13] all included a gain control computation.”
MethodBinocular Combination of Contrast Signals by Striate Cortical Neurons in the Monkey
Smith, E. L., Chino, Y. M., Ni, J., & Cheng, H. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.78.1.366 · Cited by 45
Receptive fields and suppressive fields in the early visual system
Carandini, M. (2004) · Cited by 44
Self-citationGABA-mediated inhibition correlates with orientation selectivity in primary visual cortex of cat
Li, G., Yang, Y., Liang, Z., Xia, J., Yang, Y., & Zhou, Y. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.06.032 · Cited by 44
Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits
van Rossum, M. C. W., van der Meer, M. A. A., Xiao, D., & Oram, M. W. (2008) · Neural Computation · doi:10.1162/neco.2008.06-07-546 · Cited by 47
Cognitive, Systems, and Computational Neurosciences of the Self in Motion
Noel, J., & Angelaki, D. E. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-021021-103038 · Cited by 43
“Divisive normalization is a ubiquitous neural computation wherein the output of each neuron is divided by the summed activity of a normalization pool (Carandini & Heeger 1994, 2011).”
Hearing in noisy environments: noise invariance and contrast gain control
Willmore, B. D. B., Cooke, J. E., & King, A. J. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2014.274886 · Cited by 42
“Shunting inhibition has long been considered as a mechanism for changing neuronal gain (Carandini & Heeger, 1994; Nelson, 2008).”
BackgroundSpatially tuned normalization explains attention modulation variance within neurons
Ni, A. M., & Maunsell, J. H. R. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00218.2017 · Cited by 40
“Normalization models explain the nonlinear way that neurons sum their responses to individual stimuli when those stimuli appear together (Bonds 1989; Carandini et al. 1997; Carandini and Heeger 1994; Heeger et al. 1996; Morrone et al. 1982).”
BackgroundSuppressed Patterns Alter Vision during Binocular Rivalry
Pearson, J., & Clifford, C. W. G. (2005) · Current Biology · doi:10.1016/j.cub.2005.10.066 · Cited by 43
“Although the population response at the binocular level was modeled as a linear combination of the monocular population responses, we observed qualitatively similar behavior when multiplicative interactions were implemented [37, 38].”
BackgroundOptimality of Human Contour Integration
Ernst, U. A., Mandon, S., Schinkel–Bielefeld, N., Neitzel, S. D., Kreiter, A. K., & Pawelzik, K. R. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002520 · Cited by 42
“It is known that non–linear computations on synaptic inputs are performed as early as from LGN and primary visual cortex on [52–54], and it is possible that these non–linearities provide the substrate required to compute the AND-like operations necessary for implementing Bayesian inference.”
BackgroundFeedback-induced gain control in stochastic spiking networks
Sutherland, C., Doiron, B., & Longtin, A. (2009) · Biological Cybernetics · doi:10.1007/s00422-009-0298-5 · Cited by 42
“1b) is often assumed in rate-based models of neurons (Carandini and Heeger 1994; Chance and Abbott 2000), and is a central feature in both visual and motor control (Salinas and Thier 2000).”
BackgroundSubramaniyan, M., Ecker, A. S., Patel, S. S., Cotton, R. J., Bethge, M., Pitkow, X., Berens, P., & Tolias, A. S. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00792.2017 · Cited by 44
“…lag psychophysical experiment, is not readily comparable to previous physiological studies in V1 that examined luminance or contrast effect on latency using different stimulus configurations (Carandini and Heeger 1994; Gawne et al. 1996; Maunsell and Gibson 1992; Oram 2010; Reich et al. 2001).”
ResultEffects of contrast and size on orientation discrimination
Mareschal, I., & Shapley, R. M. (2003) · Vision Research · doi:10.1016/j.visres.2003.07.009 · Cited by 42
“Typically, invariance was suggested to occur via a contrast gain control mechanism that normalized the response of a filter (receptive field) by the pooled responses of surrounding filters (Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1997).”
BackgroundRusso, F. D., & Donatella Spinelli, A. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00262-6 · Cited by 42
“Consistent with models for the mediation of contrast gain control (Shapley and Victor, 1981; Carandini and Heeger, 1994), attention increased VEP amplitudes and reduced the phase-advance with contrast.”
Orientation discrimination in human vision: Psychophysics and modeling
Beaudot, W. H. A., & Mullen, K. T. (2005) · Vision Research · doi:10.1016/j.visres.2005.10.016 · Cited by 43
“Here, we propose a variation of the biophysical normalization model of Carandini and Heeger (Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1999), in which r1/2 is a locally pooled activity that is orientation selective.”
MethodInfluentialAn intracellular study of the contrast-dependence of neuronal activity in cat visual cortex
Ahmed, B. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.6.559 · Cited by 42
Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex
Baker, D. H., & Wade, A. R. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw395 · Cited by 41
Recurrent networks in human visual cortex: psychophysical evidence
Adini, Y., & Sagi, D. (2001) · Journal of the Optical Society of America A · doi:10.1364/josaa.18.002228 · Cited by 41
Dependency Reduction with Divisive Normalization: Justification and Effectiveness
Lyu, S. (2011) · Neural Computation · doi:10.1162/neco_a_00197 · Cited by 39
“DN is believed to be implementable with the cortical neurons (Carandini & Heeger, 1994; Carandini, Heeger, & Senn, 2002), though the specific neural mechanism for such an implementation is still under debate (see Holt & Koch, 1997).”
BackgroundPersi, E., Hansel, D., Nowak, L. G., Barone, P., & van Vreeswijk, C. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001078 · Cited by 41
“However, only a few of them have examined both features together, either unsuccessfully [42] or using parameter regimes that may not be relevant to the in vivo situation (membrane time constants: [49,50]; synaptic depressions: [52,53]).”
BackgroundOrientation anisotropies in human primary visual cortex depend on contrast
Maloney, R. T., & Clifford, C. W. G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.034 · Cited by 40
“…framework for this idea in terms of a divisive normalisationmechanism that derives from earliermodels of striate gain control (e.g. Bonds, 1989, 1991; Heeger, 1992; Geisler and Albrecht, 1992; Wilson and Humanski, 1993; Carandini and Heeger, 1994; Carandini et al., 1997; Wainwright et al., 2001).”
Sheliga, B. M., Kodaka, Y., FitzGibbon, E. J., & Miles, F. A. (2006) · Vision Research · doi:10.1016/j.visres.2005.11.033 · Cited by 39
“In the above-mentioned studies on pursuit eye movements, vector averaging was assumed to result when all of the active neurons in the population make a contribution (summation) and there is a divisive normalization ( Carandini & Heeger, 1994; Heeger, Simoncelli, & Movshon, 1996; Simoncelli & Heeger, 1998), whereas win…”
BackgroundSpatiochromatic Context Modeling for Color Saliency Analysis
Zhang, J., Wang, M., Zhang, S., Li, X., & Wu, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2464316 · Cited by 39
“This is a much simpler and effective technique for robust purpose and has been widely used in saliency detection, such as in [21], [23], and [70]–[72].”
MethodSome observations on the pedestal effect
Henning, G. B., & Wichmann, F. A. (2007) · Journal of Vision · doi:10.1167/7.1.3 · Cited by 38
“If we consider divisive contrast gain control mechanisms that integrate stimuli across a broad range of spatial frequencies ( Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1997; Heeger, 1992; Schwartz & Simoncelli, 2001), the dipper is predicted to be less pronounced in the broadbandnoise condition than with…”
Hildebrandt, K. J., Benda, J., & Hennig, R. M. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2556-11.2011 · Cited by 38
“These elementary mathematical computations constitute building blocks for both the CNS (Carandini and Heeger, 1994; Peña and Konishi, 2001) and optimal encoding of stimuli in early sensory processing across modalities and species (Barlow and Levick, 1965, Laughlin, 1994; Carandini and Heeger, 1994; Harris et al., 200…”
InfluentialA comprehensive data-driven model of cat primary visual cortex
Antolík, J., Cagnol, R., Rózsa, T., Monier, C., Frégnac, Y., & Davison, A. P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012342 · Cited by 35
Orientation in Visual Cortex: A Simple Mechanism Emerges
Das, A. (1996) · Neuron · doi:10.1016/s0896-6273(00)80067-7 · Cited by 37
“This could explain how V1 cells maintain orientation (and direction) selecSelected Readingtivity notably unchanged over a wide range of stimulus intensities and contrasts (Carandini and Heeger, 1994).”
BackgroundHuman primary visual cortex (V1) is selective for second-order spatial frequency
Hallum, L. E., Landy, M. S., & Heeger, D. J. (2011) · Journal of Neurophysiology · doi:10.1152/jn.01007.2010 · Cited by 35
Self-citationTschechne, S., & Heiko Neumann, B. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00093 · Cited by 40
“Each model area consists of a three-stage processing cascade of initial filtering, application of modulatory feedback effects and center-surround interactions leading to an activity normalization (Carandini and Heeger, 1994; Carandini et al., 1999; Kouh and Poggio, 2008; Carandini and Heeger, 2012).”
BackgroundMethodShunting inhibition, a silent step in visual cortical computation
Frégnac, Y., Monier, C., Chavane, F., Baudot, P., & Graham, L. J. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2004.02.004 · Cited by 37
“A simple version is the so-called normalization model, which proposes a rescaling mechanism consisting of a feedback-driven inhibitory shunt whose strength is proportional to the average activity of a pool of excitatory neurons [9,10].”
BackgroundThe indeterminacy of computation
Fresco, N., Copeland, B. J., & Wolf, M. J. (2021) · Synthese · doi:10.1007/s11229-021-03352-9 · Cited by 34
The Neurobiology of Context-Dependent Valuation and Choice
Louie, K., & Martino, B. D. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00024-3 · Cited by 41
Normalization in human somatosensory cortex
Brouwer, G. J., Arnedo, V., Offen, S., Heeger, D. J., & Grant, A. C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00939.2014 · Cited by 36
Self-citationCook, E. P., Guest, J. A., Liang, Y., Masse, N. Y., & Colbert, C. M. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00414.2007 · Cited by 34
Hutt, A., & Buhry, L. (2014) · Journal of Computational Neuroscience · doi:10.1007/s10827-014-0512-x · Cited by 34
“This result is in good accordance with the literature on single Type-I neurons, pointing out a subtractive and divisive effect of inhibition (Carandini and Heeger 1994; Doiron et al. 2001).”
Contrast gain control in mouse auditory cortex
Cooke, J. E., King, A. J., Willmore, B. D. B., & Schnupp, J. W. H. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00847.2017 · Cited by 34
“In the mammalian cortex, it been observed in sensory areas, including primary visual (V1) (Carandini et al. 1997; Carandini and Heeger 1994; Heeger 1992; Heeger et al. 1996) and auditory (A1) (Rabinowitz et al. 2011) and somatosensory cortex (S1) (Garcia-Lazaro et al. 2007), as well as in higher…”
BackgroundVisual aftereffects and sensory nonlinearities from a single statistical framework
Laparra, V., & Malo, J. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00557 · Cited by 32
“In the language of control theory, centering is a form of additive gain control while scaling is divisive (or multiplicative) in nature, and may be nonlinear (Carandini and Heeger, 1994).”
BackgroundMethodRegularization operators for natural images based on nonlinear perception models
Gutierrez, J., Ferri, F. J., & Malo, J. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.860345 · Cited by 33
Exploiting the generic viewpoint assumption
Freeman, W. (1996) · International Journal of Computer Vision · doi:10.1007/bf00208721 · Cited by 33
“(Other local contrast response models (Albrecht and Geisler, 1991; Carandini and Heeger, 1994) could be used.”
Blakeslee, B., Cope, D. K., & McCourt, M. E. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0573-4 · Cited by 32
“Implementing local contrast normalization, as done by Robinson et al. (2007) in their LODOG version of the ODOG model, and which is characteristic of visual neurons (Carandini & Heeger, 1994; Cope et al, 2013; 2014), allows it to account for the zig-zag White stimulus.”
MethodGold, J. I., Law, C.-T., Connolly, P. M., & Bennur, S. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00744.2009 · Cited by 33
“This kind of pooling scheme can account for numerous nonlinear neural computations ( Britten and Heuer 1999 ; Car - andini and Heeger 1994 ; Carandini et al .”
BackgroundZero-dimensional noise: The best mask you never saw
Baker, D. H., & Meese, T. S. (2012) · Journal of Vision · doi:10.1167/12.10.20 · Cited by 32
“For example, it is well established that there is mutual suppression between spatiotemporally tuned channels in primary visual cortex (Bonds, 1989; Carandini & Heeger, 1994; Heeger, 1992; Morrone, Burr, & Maffei, 1982).”
BackgroundContrast induced changes in response latency depend on stimulus specificity
Oram, M. W. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.11.021 · Cited by 33
“Population based models, such as the shunting inhibition model (Carandini and Heeger, 1994), use pooling of the responses from many neurones with varying stimulus preferences (orientation, spatial frequency, phase, etc.).”
InfluentialLauritzen, T. Z., Krukowski, A. E., & Miller, K. D. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1803 · Cited by 32
“…the existence of cross-orientation inhibition has led to the suggestion that inhibition from cells tuned to dissimilar orientations plays an important role in setting the gain of cortical responses (Carandini and Heeger 1994; Carandini et al. 1997, 1999; Heeger 1992; Heeger et al. 1996).”
Noise-induced divisive gain control in neuron models
Longtin, A., Doiron, B., & Bulsara, A. R. (2002) · Biosystems · doi:10.1016/s0303-2647(02)00073-4 · Cited by 32
“Many neurons are thought to perform scaling operations on their input over a variety of time scales (Koch, 1999; Carandini and Heeger, 1994; Nelson, 1994; Salinas and Thier, 2000).”
Quadrature subunits in directionally selective simple cells: Spatiotemporal interactions
Emerson, R. C. (1997) · Visual Neuroscience · doi:10.1017/s0952523800011482 · Cited by 32
Learning Complex Cell Invariance from Natural Videos: A Plausibility Proof
Masquelier, T., Serre, T., Thorpe, S. J., & Poggio, T. (2007) · Defense Technical Information Center (DTIC) · doi:10.21236/ada477541 · Cited by 32
“Normalization mechanisms (also commonly referred to as gain control) in this case, can be achieved by a feedforward (or recurrent) shunting inhibition (Torre and Poggio, 1978; Reichardt et al., 1983; Carandini and Heeger, 1994).”
BackgroundMethodComplementary surrounds explain diverse contextual phenomena across visual modalities.
Mély, D. A., Linsley, D. A., & Serre, T. (2018) · Psychological Review · doi:10.1037/rev0000109 · Cited by 31
Estimating nonlinear receptive fields from natural images
Rapela, J., Mendel, J. M., & Grzywacz, N. M. (2006) · Journal of Vision · doi:10.1167/6.4.11 · Cited by 31
“…& Tolhurst, 1978), saturation (Dean, 1981; Maffei & Fiorentini, 1973), expansion (Albrecht & Hamilton, 1982; Sclar & Freeman, 1982), phase advance (Carandini & Heeger, 1994; Dean & Tolhurst, 1986), and cross-orientation inhibition (Bonds, 1989; Morrone, Burr, & Maffei, 1982), to mention a few.”
Ledgeway, T., Zhan, C. A., Johnson, A. P., SONG, Y., & Baker, C. L. (2005) · Visual Neuroscience · doi:10.1017/s0952523805221120 · Cited by 31
“…by saturation (Robson, 1975) is largely prevented by a contrast gain control in striate cortex, which preserves the CRF shape for nonoptimal stimuli (Albrecht & Hamilton, 1982; Skottun et al., 1987; Albrecht & Geisler, 1991; Bonds, 1991; Geisler & Albrecht, 1992; Carandini & Heeger, 1994).”
BackgroundFinding boundaries in natural images: A new method using point descriptors and area completion
Belongie, S., & Malik, J. (1998) · Lecture notes in computer science · doi:10.1007/bfb0055702 · Cited by 31
“This can be related to the model of local gain control in primary visual cortex simple cells discussed in [5].”
BackgroundAsymmetric dynamics of adaptation after onset and offset of flicker
Snippe, H. P., Poot, L., & van Hateren, J. H. (2004) · Journal of Vision · doi:10.1167/4.1.1 · Cited by 31
“In the present paper, we study the dynamics of such a contrast gain control, by measuring detection thresholds for tests that are presented shortly after an onset of the background flicker (Wolfson & Graham, 2000; Snowden, 2001), and also after the offset of the background flicker ( Foley & Boynton, 1993 ).”
BackgroundResultInfluentialEpifanio, I., Gutiérrez, J., & Malo, J. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00325-4 · Cited by 31
Temporal detection in human vision: dependence on stimulus energy
Fredericksen, R. E., & Hess, R. F. (1997) · Journal of the Optical Society of America A · doi:10.1364/josaa.14.002557 · Cited by 31
An analysis of the temporal integration mechanism in human motion perception
Fredericksen, R. E., Verstraten, F. A. J., & van de GRIND, W. A. (1994) · Vision Research · doi:10.1016/0042-6989(94)90081-7 · Cited by 31
Wang, B., Han, C., Wang, T., Dai, W., Li, Y., Yang, Y., Yang, G., Zhong, L., Zhang, Y., Wu, Y., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83923-5 · Cited by 29
Abnormal inhibition-excitation imbalance in migraine
Nguyen, B. N., McKendrick, A. M., & Vingrys, A. J. (2015) · Cephalalgia · doi:10.1177/0333102415576725 · Cited by 30
“As contrast rises, the cell’s excitatory response is increasingly inhibited by a ‘normalisation pool’ of neighbouring neurones (2,3).”
BackgroundA neural implementation of Bayesian inference based on predictive coding
Spratling, M. (2016) · Connection Science · doi:10.1080/09540091.2016.1243655 · Cited by 32
“Neurons that perform multiplicative (Salinas and Sejnowski, 2001; Salinas and Thier, 2000), and divisive (Carandini and Heeger, 1994; Heeger, 1992), operations are common throughout the brain.”
BackgroundThe role of synapses in cortical computation
Douglas, R. J., Mahowald, M. A., Martin, K. A. C., & Stratford, K. J. (1996) · Journal of Neurocytology · doi:10.1007/bf02284849 · Cited by 30
“…sodium and calcium conductances in the dendrites, which may also serve to amplify the synaptic inputs to different parts of the dendritic tree (Shepherd et ø1., 7985; Huguenard et aL, 7989; Pockberger, 1991; Bernander et ø1., 1994; Hirsch et ø1., 7995; Markram & Sakmann, 7994; Yuste et ø1., 7994 ).”
BackgroundHierarchical Models of the Visual System
Serre, T. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_345 · Cited by 29
A divisive model of evidence accumulation explains uneven weighting of evidence over time
Keung, W., Hagen, T. A., & Wilson, R. C. (2020) · Nature Communications · doi:10.1038/s41467-020-15630-0 · Cited by 33
Ayzenshtat, I., Gilad, A., Zurawel, G., & Slovin, H. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1596-12.2012 · Cited by 29
“The first phase was an early and rapid increase, starting 40 ms after the stimulus onset, reflecting an increase in neural population activity.”
BackgroundPossible Role of Dendritic Compartmentalization in the Spatial Working Memory Circuit
Morita, K. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0059-08.2008 · Cited by 31
“Without dendritic compartmentalization, recurrent inhibition itself possesses the ability to implement input normalization, as proposed in the models of sensory processing circuits (Carandini and Heeger, 1994; Hahnloser et al., 2000).”
MethodNeural Mechanisms for the Robust Representation of Junctions
Hansen, T., & Heiko Neumann, B. (2004) · Neural Computation · doi:10.1162/089976604773135087 · Cited by 29
“Shunting inhibition has been proposed as a possible mechanism for divisive interactions in a number of studies (Borg-Graham et al., 1998; Carandini and Heeger, 1994).”
BackgroundSpatiotemporal dynamics of neuronal population response in the primary visual cortex
Zhou, D., Rangan, A. V., McLaughlin, D. W., & Cai, D. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1308167110 · Cited by 29
“As the contrast increases, the reaction time for the V1 network dynamics will be accelerated (Discussion) similar to what was observed in single neuron studies (19, 20).”
Order of operations for decoding superior colliculus activity for saccade generation
Katnani, H. A., & Gandhi, N. J. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00265.2011 · Cited by 28
“The nature of this conversion causes the output of an averaging scheme to become dependent on whether its computation occurs before or after the exponential transformation.”
BackgroundHansen, B. C., & Essock, E. A. (2006) · Vision Research · doi:10.1016/j.visres.2006.07.016 · Cited by 27
“This form of cortical normalization was primarily introduced by Carandini and Heeger (1994), Heeger (1992a, 1992b, 1993), and gained support from subtly different models proposed by Carandini, Heeger, and Movshon (1997, 1999), Simoncelli and Schwartz (1999), Wilson and Kim (1998),Wilson, Loffler,…”
MethodSpatiotemporal Response Properties of Optic-Flow Processing Neurons
Weber, F., Machens, C. K., & Borst, A. (2010) · Neuron · doi:10.1016/j.neuron.2010.07.017 · Cited by 28
“Various studies have pointed out the importance of divisive normalization for explaining the tuning properties of cortical visual neurons (Carandini and Heeger, 1994; Rust et al., 2006), the processing of natural images (Schwartz and Simoncelli, 2001), and olfactory signals (Borst, 1983; Olsen et al.”
Sheliga, B. M., Quaia, C., FitzGibbon, E. J., & Cumming, B. G. (2016) · Journal of Vision · doi:10.1167/16.1.8 · Cited by 27
“This is puzzling because many previous studies of interactions between Fourier components report nonlinear interactions (Britten & Heuer, 1999; Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1997; Heuer & Britten, 2002; Kumbhani et al., 2008; Liu & Sperling, 2006; Miura et al., 2014b;…”
Contrast Adaptation Contributes to Contrast-Invariance of Orientation Tuning of Primate V1 Cells
Nowak, L. G., & Barone, P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004781 · Cited by 27
“tuning therefore constitutes a strong constraint for understanding mechanisms underlying generation of orientation selectivity and has been the cornerstone in numerous modeling studies attempting to explain generation of orientation tuning [9,13–25].”
BackgroundExtraction of Surface-Related Features in a Recurrent Model of V1-V2 Interactions
Weidenbacher, U., & Heiko Neumann, B. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005909 · Cited by 26
“Such a stage of divisive inhibition has been previously proposed to account for nonlinear effects in contrast and motion responses of V1 cells [20,21,22].”
BackgroundLiu, K., & Yao, H. (2014) · European Journal of Neuroscience · doi:10.1111/ejn.12567 · Cited by 25
“…curve, spatial frequency tuning, temporal frequency tuning, response phase, direction selectivity and speed tuning (Dean & Tolhurst, 1986; Carandini & Heeger, 1994; Levitt & Lund, 1997; Kapadia et al., 1999; Sceniak et al., 1999, 2002; Alitto & Usrey, 2004; Guo et al., 2005; Peterson et…”
BackgroundUsing Evolutionary Algorithms for Fitting High-Dimensional Models to Neuronal Data
Svensson, C., Coombes, S., & Peirce, J. W. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9140-7 · Cited by 26
“This filter effects the shape of the contrast tuning curve and also works in a divisive way (Heeger 1992; Carandini and Heeger 1994; Carandini et al. 1997).”
Hood, D. C., Ghadiali, Q., Zhang, J. C., Graham, N., Wolfson, S., & Zhang, X. (2006) · Journal of Vision · doi:10.1167/6.5.4 · Cited by 26
Muscarinic and Nicotinic Contribution to Contrast Sensitivity of Macaque Area V1 Neurons
Herrero, J. L., Gieselmann, M. A., & Thiele, A. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2017.00106 · Cited by 25
“However, contrast normalization is a well-established phenomenon in V1 (Carandini and Heeger, 1994, 2012; Carandini et al., 1997; Tolhurst and Heeger, 1997; Albrecht et al., 2002), and if cholinergic mechanisms were a main driver thereof we should have been able to uncover these with our models.”
BackgroundDeconstructing Interocular Suppression: Attention and Divisive Normalization
Li, H.-H., Carrasco, M., & Heeger, D. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004510 · Cited by 27
“To compute the neural responses, the excitatory drive of each neuron was multiplied by its attentional gain factor [12] and then divided by its suppressive drive [5,6,12,32].”
MethodSelf-citationFunctional Mechanisms Encoding Others' Direction of Gaze in the Human Nervous System
Palmer, C. J., & Clifford, C. W. G. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01150 · Cited by 22
“…cross-channel response is divisive normalization, which is a form of gain control that has been proposed as a canonical computational feature of neural responses in the visual cortex (Carandini & Heeger, 1994, 2012) but is yet to be demonstrated for gaze processing or social vision more broadly.”
BackgroundElman Backpropagation as Reinforcement for Simple Recurrent Networks
Grüning, A. (2007) · Neural Computation · doi:10.1162/neco.2007.19.11.3108 · Cited by 25
“Positive evidence for pools of neurons doing precisely such a divisive normalisation is summarised in Carandini and Heeger (1994). Furthermore there needs to be a competitive process among the output neurons to finally select one according to Qy as the winning neuron.”
BackgroundMultiplicative Mechanism of Lateral Interactions Revealed by Controlling Interhemispheric Input
Wunderle, T., Eriksson, D., & Schmidt, K. E. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs081 · Cited by 24
Perceptual Texture Dimensions Modulate Neuronal Response Dynamics in Visual Cortical Area V4
Kim, T., Bair, W., & Pasupathy, A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0971-21.2021 · Cited by 24
A theory of autism bridging across levels of description
Noel, J., & Angelaki, D. E. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.04.010 · Cited by 27
“…control effectively modulates synaptic weights and is considered a canonical neural computation [56] in that it has been described across primary [55] and extrastriate [57] visual cortex, auditory cortex [58], the superior colliculus [59], and the antenna lobe of fruit fl ies [60], among others.”
InfluentialSheliga, B. M., FitzGibbon, E. J., & Miles, F. A. (2008) · Progress in brain research · doi:10.1016/s0079-6123(08)00633-x · Cited by 24
“Rather, we postulate an active process like the divisive normalization often described in visual-motionsensitive neurons in the cortex and generally attributed to some global inhibitory process (Heeger, 1992; Carandini and Heeger, 1994; Carandini et al., 1997; Simoncelli and Heeger, 1998; Britten and Heuer, 1999; Heue…”
BackgroundBouecke, J. D., Tlapale, É., Kornprobst, P., & Heiko Neumann, B. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2011/781561 · Cited by 24
“Simoncelli and Heeger [50] proposed a model of detecting motion energy in areas V1 and MT using linear spatiotemporal filters.”
BackgroundMethodInfluentialAn Equivalent Noise Investigation of Saccadic Suppression
Watson, T., & Krekelberg, B. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6255-10.2011 · Cited by 24
“Given the similarity in name, it is important to point out that a gain change in the perceptual template model is qualitatively different from contrast gain control—a mechanism whose role is well established in visual processing (Heeger, 1992; Carandini and Heeger, 1994; Carandini et al., 1997).”
BackgroundIn Praise of Artifice Reloaded: Caution With Natural Image Databases in Modeling Vision
Martínez-Garcia, M., Bertalmı́o, M., & Malo, J. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00008 · Cited by 23
“…of the nonlinearity is a distinct feature of the masking effect (Foley, 1994; Watson and Solomon, 1997), and more importantly, it is a distinct feature of real neurons (Carandini and Heeger, 1994, 2012) with regard to the simplified neurons used in deep learning (Goodfellow et al., 2016).”
Background
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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