Intracellular Measurements of Spatial Integration and the MAX Operation in Complex Cells of the Cat Primary Visual Cortex
Abstract
We have examined the spatial integration properties of complex cells to determine whether some of their responses can be described by a maximum operation (MAX)-like computation, as suggested by Riesenhuber and Poggio's model of object recognition. Membrane potential was recorded from anesthetized cats while optimally oriented bars were presented, either alone or in pairs, in different parts of the cells' receptive field. In most cells, the membrane potential response to two bars presented simultaneously could not be predicted by the sum of the responses to individual bars. In many cells, however, the responses closely approximated a MAX-like model. That is, the response of the cell to two bars was similar to the larger of the two individual responses (“soft-MAX”). The degree of nonlinear summation varied from cell to cell and varied within single cells from one stimulus configuration to another but on average fit most closely to the MAX model. The firing response of the cells was also well predicted by the MAX-like model. The MAX-like behavior was independent of the distance between the bars (orthogonal to the preferred orientation), independent of the relative amplitude of the responses, and slightly less pronounced at low levels of contrast. This MAX-like behavior of a subset of complex cells may play an important role in invariant object recognition in clutter.
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The model predicts, at the C1 and C2 levels, respectively, the max-like behavior of a subclass of complex cells in V1 and V4.
“For instance, the model predicts (23), at the C 1 and C 2 levels, respectively, the max-like behavior of a subclass of complex cells in V1 (26) and V4 (27).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [26]
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 · see citing works
The VideoToolbox software for visual psychophysics: transforming numbers into movies
Pelli, D. G. (1997) · Spatial Vision · doi:10.1163/156856897x00366 · Cited by 11,110 · see citing works
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442 · see citing works
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309 · see citing works
Self-citationNormalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835 · see citing works
Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex
Carandini, M., Heeger, D. J., & Movshon, J. A. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-21-08621.1997 · Cited by 926 · see citing works
Action potential initiation and backpropagation in neurons of the mammalian CNS
Stuart, G., Spruston, N., Sakmann, B., & Häusser, M. (1997) · Trends in Neurosciences · doi:10.1016/s0166-2236(96)10075-8 · Cited by 768 · see citing works
Laminar differences in receptive field properties of cells in cat primary visual cortex
Gilbert, C. D. (1977) · The Journal of Physiology · doi:10.1113/jphysiol.1977.sp011863 · Cited by 670 · see citing works
Visual input evokes transient and strong shunting inhibition in visual cortical neurons
Graham, L. J., Monier, C., & Frégnac, Y. (1998) · Nature · doi:10.1038/30735 · Cited by 669 · see citing works
Summation and Division by Neurons in Primate Visual Cortex
Carandini, M., & Heeger, D. J. (1994) · Science · doi:10.1126/science.8191289 · Cited by 649 · see citing works
Receptive field organization of complex 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.sp012489 · Cited by 623 · see citing works
Contrast's effect on spatial summation by macaque V1 neurons
Sceniak, M. P., Ringach, D. L., Hawken, M. J., & Shapley, R. M. (1999) · Nature Neuroscience · doi:10.1038/11197 · Cited by 608 · see citing works
Membrane Potential and Firing Rate in Cat Primary Visual Cortex
Carandini, M., & Ferster, D. (2000) · Journal of Neuroscience · doi:10.1523/jneurosci.20-01-00470.2000 · Cited by 413 · see citing works
Self-citationFunctional connectivity between simple cells and complex cells in cat striate cortex
Alonso, J.-M., & Martinez, L. M. (1998) · Nature Neuroscience · doi:10.1038/1609 · Cited by 278 · see citing works
EPSP-IPSP interactions in cat visual cortex studied with in vivo whole- cell patch recording
Ferster, D., & Jagadeesh, B. (1992) · Journal of Neuroscience · doi:10.1523/jneurosci.12-04-01262.1992 · Cited by 254 · see citing works
Self-citationHypercomplex and simple/complex cell classifications in cat striate cortex.
Kato, H., Bishop, P. O., & Orban, G. A. (1978) · Journal of Neurophysiology · doi:10.1152/jn.1978.41.5.1071 · Cited by 219 · see citing works
Neural Mechanisms for Processing Binocular Information I. Simple Cells
Anzai, A., Ohzawa, I., & Freeman, R. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.82.2.891 · Cited by 212 · see citing works
Nonlinear directionally selective subunits in complex cells of cat striate cortex
Emerson, R. C., Citron, M. C., Vaughn, W. J., & Klein, S. A. (1987) · Journal of Neurophysiology · doi:10.1152/jn.1987.58.1.33 · Cited by 201 · see citing works
Are Cortical Models Really Bound by the “Binding Problem”?
Riesenhuber, M., & Poggio, T. (1999) · Neuron · doi:10.1016/s0896-6273(00)80824-7 · Cited by 192 · see citing works
Self-citationHypercomplex cells in the cat's striate cortex.
Dreher, B. (1972) · PubMed · Cited by 142 · see citing works
Responses of Primate Visual Cortical V4 Neurons to Simultaneously Presented Stimuli
Gawne, T. J., & Martin, J. M. (2002) · Journal of Neurophysiology · doi:10.1152/jn.2002.88.3.1128 · Cited by 134 · see citing works
Sato, T. (1989) · Experimental Brain Research · doi:10.1007/bf00250563 · Cited by 125 · see citing works
Gawne, T. J., & Martin, J. M. (2002) · Journal of Neurophysiology · doi:10.1152/jn.00151.200 · Cited by 123 · see citing works
Anderson, J. S., Lampl, I., Gillespie, D. C., & Ferster, D. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-06-02104.2001 · Cited by 101 · see citing works
Self-citationResponses of single units in cat visual cortex to moving bars of light as a function of bar length
Rose, D. H. (1977) · The Journal of Physiology · doi:10.1113/jphysiol.1977.sp011987 · Cited by 98 · see citing works
The two-dimensional spatial structure of nonlinear subunits in the receptive fields of complex cells
Szulborski, R. G., & Palmer, L. A. (1990) · Vision Research · doi:10.1016/0042-6989(90)90040-r · Cited by 94 · see citing works
Biophysiologically Plausible Implementations of the Maximum Operation
Yu, A. J., Giese, M. A., & Poggio, T. (2002) · Neural Computation · doi:10.1162/089976602760805313 · Cited by 94 · see citing works
Self-citationNeural Mechanisms for Processing Binocular Information II. Complex Cells
Anzai, A., Ohzawa, I., & Freeman, R. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.82.2.909 · Cited by 88 · see citing works
Lampl, I., Anderson, J. S., Gillespie, D. C., & Ferster, D. (2001) · Neuron · doi:10.1016/s0896-6273(01)00278-1 · Cited by 82 · see citing works
Self-citationSpatial summation of responses in receptive fields of single cells in cat striate cortex
Henry, G. H., Goodwin, A. W., & Bishop, P. O. (1978) · Experimental Brain Research · doi:10.1007/bf00239730 · Cited by 77 · see citing works
Construction of Complex Receptive Fields in Cat Primary Visual Cortex
Martı́nez, L. M., & Alonso, J. (2001) · Neuron · doi:10.1016/s0896-6273(01)00489-5 · Cited by 75 · see citing works
Spatial pooling in the second-order spatial structure of cortical complex cells
Sakai, K., & Tanaka, S. (2000) · Vision Research · doi:10.1016/s0042-6989(99)00230-8 · Cited by 40 · see citing works
Baker, C. L. (2001) · Visual Neuroscience · doi:10.1017/s0952523801183136 · Cited by 25 · see citing works
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Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355 · see citing works
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Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598681 · Cited by 3,367 · see citing works
“[33] proposed that spatial integration properties of complex cells can be described by a series of pooling operations.”
BackgroundRobust Object Recognition with Cortex-Like Mechanisms
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.56 · Cited by 1,667 · see citing works
“1), respectively, the max-like behavior of a subclass of complex cells in V1 [31] and cells in V4 [32].”
BackgroundSelf-citationA feedforward architecture accounts for rapid categorization
Serre, T., Oliva, A., & Poggio, T. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700622104 · Cited by 918 · see citing works
Self-citationObject Recognition with Features Inspired by Visual Cortex
Serre, T., Wolf, L., & Poggio, T. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.254 · Cited by 841 · see citing works
“in favor of the max operation has appeared [6, 9].”
BackgroundSelf-citationThe uncrowded window of object recognition
Pelli, D. G., & Tillman, K. A. (2008) · Nature Neuroscience · doi:10.1038/nn.2187 · Cited by 659 · see citing works
Kiani, R., Esteky, H., Mirpour, K., & Tanaka, K. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00024.2007 · Cited by 495 · see citing works
“The receptive fields of complex cells were modeled by MAX operation (Lampl et al. 2004; Riesenhuber and Poggio 1999) on outputs of neighboring simple cells with similar orientation selectivity .”
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DeWitt, I., & Rauschecker, J. P. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113427109 · Cited by 459 · see citing works
“Spatial pooling is well documented in visual cortex (7, 31, 32) and there is some evidence for its analog, spectrotemporal pooling, in auditory cortex (33–35), including the observation of complex cells when A1 is developmentally reprogrammed as a surrogate V1 (36).”
BackgroundA quantitative theory of immediate visual recognition
Serre, T., Kreiman, G., Kouh, M., Cadieu, C. F., Knoblich, U., & Poggio, T. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65004-8 · Cited by 217 · see citing works
“The existence of a MAX operation in visual cortex was proposed by Riesenhuber and Poggio (1999) from theoretical arguments [and limited experimental evidence (Sato, 1989)] and was later supported experimentally in both V4 (Gawne and Martin, 2002) and V1 at the complex cell level (Lampl et al., 2004).”
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“However, mechanisms that produce averaging behavior may not be the only ones mediating stimulus interactions, because some populations of cat V1 neurons (Lampl et al., 2004) and monkey V4 neurons (Gawne and Martin, 2002) have responses that are similar to the response of the best constituent stimulus of each pair (CCI…”
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“From the raw image on the retina, local orientation is extracted (area V1), pooled over spatial neighborhoods (V2/V4), and filtered with learned complex features (V4/I).”
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“The luminance images (175× 175 pixels, 2.9˚ visual angle) were given as input to a population of modeled V1 simple and complex cells (Riesenhuber and Poggio, 2002; Lampl et al., 2004; Kiani et al., 2007).”
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“…complex cells in primary visual cortex, pool over sets of S1 cells of the same preferred orientation (using a maximum, ‘‘MAX,’’ pooling function [Lampl et al., 2004]) with similar preferred spatial frequencies and receptive field locations, to increase receptive field size and broaden…”
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“A few recent physiological experiments (Gawne & Martin, 2002; Lampl et al., 2004) have reported that when multiple stimuli are simultaneously presented, some neurons produce a max-like response.”
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“[130] proposed that spatial integration of complex cell in visual cortex can be described by a series of pooling operations.”
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“In this rule, tmax is the reference time for updating synaptic weights.”
BackgroundCritical features for the recognition of biological motion
Casile, A., & Giese, M. A. (2005) · Journal of Vision · doi:10.1167/5.4.6 · Cited by 146 · see citing works
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Priebe, N. J. (2016) · Annual Review of Vision Science · doi:10.1146/annurev-vision-111815-114456 · Cited by 125 · see citing works
“Yet, receptive field diversity beyond the paradigm of Hubel and Wiesel may subserve flexible object recognition and is therefore also interpretable in terms of scene analysis (Riesenhuber & Poggio 1999, Lampl et al. 2004, Ullman & Bart 2004).”
BackgroundSerre, T., & Riesenhuber, M. (2004) · doi:10.21236/ada459692 · Cited by 126 · see citing works
“Additionally, biophysically plausible implementations of the MAX operation have been proposed [5] and neurons performing a MAX operation have been found in area V4 in the primate [6], and very recently also in complex cells in cat visual cortex [7].”
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Scholl, B., Wilson, D. E., & Fitzpatrick, D. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.017 · Cited by 113 · see citing works
“2 Neuron 96, 1–12, December 6, 2017 spines (Figures 1A and 1B) for individual neurons using sparse one-dimensional (1D) bars of light (Movshon et al., 1978; Lampl et al., 2004).”
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Poggio, T., & Serre, T. (2006) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 85 · see citing works
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“Biological evidence [Lampl et al. , 2004] of neurons performing MAX-like operation have been found in a subclass of CCs in V1 and V4.”
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“[19] examined that the membrane potential response to two bars presented simultaneously could not be predicted by the sum of the responses, but could be closely approximated by a maximum operation in most cells.”
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“[51] suggested that the spatial integration properties of complex cells can be described by a series of pooling operations.”
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“This is in line with findings of [84] that reported an inverse relationship between”
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Litvak, S., & Ullman, S. (2009) · Neural Computation · doi:10.1162/neco.2009.05-08-783 · Cited by 46 · see citing works
“Max-like responses are reported in cortical neurons in V1 and V4 of the cat visual cortex (Gawne & Martin, 2002 ; Lampl, Ferster, Poggio, & Riesenhuber, 2004 ).”
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“In the meantime, experimental evidence in favor of the max operation has appeared [19] [20].”
BackgroundSelf-citationSoto, F. A., & Wasserman, E. A. (2011) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-011-0070-x · Cited by 45 · see citing works
“…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, 2002; Lampl, Ferster, Poggio, & Riesenhuber, 2004).”
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“…multiplespatial-frequencychannelsorthroughadifferentnet-workmechanism(Ohzawaetal.,1990;Meletal.,1998;Chanceet al.,1999;Taoetal.,2004;Serreetal.,2007),ourobservationthat asubsetofcomplexcellsintheprimaryvisualcortexcomputea MAXoperationoverawidevarietyofstimuliaffirmstheconclu-sionofLampletal.”
BackgroundMethodInfluentialSelf-citationA single functional model accounts for the distinct properties of suppression in cortical area V1
Spratling, M. (2011) · Vision Research · doi:10.1016/j.visres.2011.01.017 · Cited by 35 · see citing works
“There is some neurophysiological support for this model (Gawne and Martin, 2002; Kouh and Poggio, 2008; Lampl et al., 2004).”
Xiao, J., & Huang, X. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2175-15.2015 · Cited by 35 · see citing works
“Another possibility involves a soft MAX-like operation (Riesenhuber and Poggio, 1999; Lampl et al., 2004) in which the neuronal response elicited by two stimuli is close to the stronger component response.”
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“…referred to as “physiological summation,” though we prefer the term “signal combination” as this makes it distinct from the alternative MAX rule (signal selection), which, in principle, could have a physiological implementation at an early sensory stage (Finn & Ferster, 2007; Lampl et al., 2004).”
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“The existence of a MAX operation in visual cortex was predicted by (Riesenhuber and Poggio, 1999) from theoretical arguments (and limited experimental evidence (Sato, 1989) and was later supported experimentally in V4 (Gawne and Martin, 2002) and in V1 at the complex cell level (Lampl et al., 2004).”
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“been validated in numerous behavioral (Serre, Oliva, & Poggio, 2007), fMRI (Jiang et al., 2006, 2007), and electrophysiological (Cadieu et al., 2007; Freedman et al., 2003; Lampl, Ferster, Poggio, & Riesenhuber, 2004; Logothetis et al., 1995; Riesenhuber & Poggio, 1999b; Serre, Kreiman, Kouh, Cadieu, & Knoblich, 2007)…”
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“Interactions between spatially separate components of the receptive fields have been studied in other sensory modalities (Heggelund, 1981; Sillito et al., 1995; Lampl et al., 2004) in order to identify nonlinearities of spatial integration.”
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“2005) or at a rate that resembles the highest response obtained with any single target, also known as a winner-take-all (WTA) algorithm (Finn and Ferster 2007; Gawne and Martin 2002; Lampl et al. 2004).”
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“However, similar findings have been reported across multiple visual cortical areas (Recanzone et al., 1997; Britten and Heuer, 1999; Gawne and Martin, 2002; Lampl, 2004; Zoccolan et al., 2005; but see Finn and Ferster, 2007; Maunsell, 2015).”
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“Physiological support for this model is found in Gawne and Martin (2002), Lampl et al. (2004), Finn and Ferster (2007), and Kouh and Poggio (2008).”
MethodBiophysical Models of Neural Computation: Max and Tuning Circuits
Knoblich, U., Bouvrie, J., & Poggio, T. (2007) · Springer eBooks · doi:10.1007/978-3-540-77028-2_9 · Cited by 14 · see citing works
“In order to improve the match with physiological data, we plan to extend our models to be more faithful to the morphology, biophysical properties and connectivity of different specific subtypes of cortical neurons in layers IV and II/III of somatosensory and visual cortices, utilizing the limited quantitative data ava…”
BackgroundMethodSelf-citationSpatial and Temporal Features of Synaptic to Discharge Receptive Field Transformation in Cat Area 17
Nowak, L. G., Sanchez-Vives, M. V., & McCormick, D. A. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90946.2008 · Cited by 13 · see citing works
“…et al. 1998; Bringuier et al. 1999; Cardin et al. 2005; Creutzfeldt and Ito 1968; Ferster 1988; Finn et al. 2007; Hirsch et al. 1998, 2002, 2003; Lampl et al. 2001, 2004; Martinez et al. 2002, 2005; Nelson et al. 1994; Nowak et al. 2005; Pei et al. 1994; Priebe and Ferster 2005; Priebe et al.…”
Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons
Yu, Q., Tang, H., Hu, J., & Tan, K. C. (2017) · Intelligent systems reference library · doi:10.1007/978-3-319-55310-8_2 · Cited by 13 · see citing works
Central auditory neurons display flexible feature recombination functions
Kozlov, A. S., & Gentner, T. Q. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00637.2013 · Cited by 12 · see citing works
“Similar to the results in visual cortex (Lampl et al. 2004), we observed AND-like responses and OR-like responses (Fig.”
BackgroundMethodResultInfluentialThe Role of Feedback in a Hierarchical Model of Object Perception
Durá-Bernal, S., Wennekers, T., & Denham, S. L. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-0164-3_14 · Cited by 12 · see citing works
“In relation to the model operations, neurons in area V4 in the primate [7] and complex cells in the cat visual cortex [20] have both been found to perform a MAX operation.”
MethodLiu, S.-Y., Hao, Q., Zhang, Y.-T., Gao, F., Song, H., Jiang, Y., Wang, Y.-S., Cui, X.-Y., & Gao, K. (2022) · Defence Technology · doi:10.1016/j.dt.2022.05.008 · Cited by 11 · see citing works
A Differential Model of the Complex Cell
Hansard, M. E., & Horaud, R. (2011) · Neural Computation · doi:10.1162/neco_a_00163 · Cited by 11 · see citing works
“There is experimental evidence for similar arrangements, with respect to both simple and complex cells (Carandini & Heeger, 1994; Heeger, 1992a; Lampl et al., 2004).”
BackgroundAsymmetric transfer of perceptual learning of luminance- and contrast-modulated motion
Petrov, A. A., & Hayes, T. R. (2010) · Journal of Vision · doi:10.1167/10.14.11 · Cited by 11 · see citing works
Object Recognition in Cortex: Neural Mechanisms, and Possible Roles for Attention
Riesenhuber, M. (2005) · Neurobiology of Attention · doi:10.1016/b978-012375731-9/50050-1 · Cited by 11 · see citing works
Self-citationA perceptually relevant shearlet-based adaptation of the PSNR
Bosse, S., Siekmann, M., Samek, W., & Wiegand, T. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296294 · Cited by 10 · see citing works
“This kind of pooling is also neurophysiologically plausible for modeling visual processing in the human brain [19].”
BackgroundBiologically inspired makeup detection system with application in face recognition
Rasti, S., Yazdi, M., & Masnadi‐Shirazi, M. A. (2018) · IET Biometrics · doi:10.1049/iet-bmt.2018.5059 · Cited by 10 · see citing works
An Analysis of the Connections Between Layers of Deep Neural NetworksPreprint
Culurciello, E., Jin, J., Dündar, A., & Bates, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.0152 · Cited by 10 · see citing works
“We used max pooling layer which is a bio-inspired approach to reducing the dimensionality of the data at each layer [30].”
MethodA Strong Constraint to the Joint Processing of Pairs of Cortical Signals
Ghose, K., & Maunsell, J. H. R. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2186-12.2012 · Cited by 11 · see citing works
“Some experiments report primarily max-like responses (often described as winner-take-all) in both striate and extrastriate visual cortex, as well as posterior parietal cortex (Gawne and Martin, 2002; Rolls et al., 2003; Lampl et al., 2004; Oleksiak et al., 2011).”
BackgroundAttentive processing improves object recognition
Chikkerur, S. S., Poggio, T., & Serre, T. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 9 · see citing works
Self-citationJalali, S., Seekings, P. J., Tan, C., Tan, H. Z. W., Lim, J., & Taylor, E. A. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6707084 · Cited by 9 · see citing works
“More specifically, evidence for the MAX-like (or ORlike) pooling used by the model has been found in cats and monkeys [16], [17], [18].”
MethodKato, D., Baba, M., Sasaki, K. S., & Ohzawa, I. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0266 · Cited by 9 · see citing works
Throwing Down the Visual Intelligence Gauntlet
Tan, C., Leibo, J. Z., & Poggio, T. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-28661-2_1 · Cited by 8 · see citing works
“Beginning in the primary visual cortex, an electrophysiology study in cats uncovered evidence that the brain employs an OR-like pooling operation, predicted by Riesenhuber and Poggio [28] as the mechanism by which complex cell receptive fields are built from simple cells [20].”
BackgroundSelf-citationYousefi, B., & Loo, C. K. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/238234 · Cited by 8 · see citing works
Hemery, E., & Aucouturier, J. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00080 · Cited by 8 · see citing works
“In particular, there has been some recent convergence in the convolutional vision model literature over max-pooling, which seems to outperform average-pooling when data sparsity is high (Boureau et al., 2010), and appears closer to physiology in the cat primary visual cortex (Lampl et al., 2004).”
BackgroundShearlet-based reduced reference image quality assessment
Bosse, S., Chen, Q., Siekmann, M., Samek, W., & Wiegand, T. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532719 · Cited by 8 · see citing works
“This kind of pooling is also neurophysiologically plausible for modeling visual processing in the human brain [26].”
BackgroundTangent Bundle Curve Completion with Locally Connected Parallel Networks
Ben-Yosef, G., & Ben‐Shahar, O. (2012) · Neural Computation · doi:10.1162/neco_a_00365 · Cited by 7 · see citing works
An integrative, experience-based theory of attentional control
Wilder, M., Mozer, M. C., & Wickens, C. D. (2011) · Journal of Vision · doi:10.1167/11.2.8 · Cited by 7 · see citing works
A Neurodynamic Model of Feature-Based Spatial Selection
Marić, M., & Domijan, D. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00417 · Cited by 7 · see citing works
“Furthermore, Lampl et al. (2004) directly measured membrane potentials in the complex cells of the cat primary visual cortex and found evidence for the MAX-like behavior in response to the pair of optimal bars.”
BackgroundNovel neural circuit mechanism for visual edge detection
Stevens, C. F. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422673112 · Cited by 7 · see citing works
A simple model of the attentional blink and its modulation by mental training
Amir, N., Tishby, N., & Nelken, I. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010398 · Cited by 6 · see citing works
“Interestingly, sub-linear integration mechanisms have previously been implied in ‘max’ operations [28] and orientation selectivity [30] in the primary visual cortex.”
BackgroundNumerosity as a topological invariant
Kluth, T., & Zetzsche, C. (2016) · Journal of Vision · doi:10.1167/16.3.30 · Cited by 6 · see citing works
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting
Coventry, B. S., Parthasarathy, A., Sommer, A. L., & Bartlett, E. L. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0628-2 · Cited by 6 · see citing works
“The four corners social network is fully-connected locally but sparsely connected between neighborhoods (Mendes et al. 2004) (Oleksiak et al. 2011, Lampl et al. 2004, Van de Heuvel and Sporns 2011).”
Yousefi, B., & Loo, C. K. (2016) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2016.081323 · Cited by 6 · see citing works
Billock, V. A. (2018) · Journal of the Optical Society of America A · doi:10.1364/josaa.35.00b267 · Cited by 6 · see citing works
A Differential Model of the Complex Cell
Hansard, M. E., & Horaud, R. (2011) · Cited by 6 · see citing works
A biologically inspired system for fast handwritten digit recognition
Wang, Z., Huang, Y., Luo, S., & Wang, L. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6115798 · Cited by 5 · see citing works
“PCICA algorithm bases on the ICA by means of its good properties in fast and simple inference, and is derived by utilizing of the properties of complex cells in V1[2, 3].”
MethodResultHuman age estimation using Multi-Frequency Biologically Inspired Features (MF-BIF)
Angulu, R., Tapamo, J., & Adewumi, A. O. (2017) · IEEE AFRICON · doi:10.1109/afrcon.2017.8095450 · Cited by 5 · see citing works
“[39] proposed that spatial integration of complex cell in visual cortex that can be described by a series of pooling operations.”
BackgroundCombining Densely Sampled Form and Motion for Human Action Recognition
Schindler, K., & Gool, L. V. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69321-5_13 · Cited by 5 · see citing works
“Using the max , rather than averaging responses, was originally proposed by [12] and has been strongly advocated by [22], because of its ability to preserve contrast features and its conformity with electro-physiological measurements [13,17].”
BackgroundBio-Inspired Presentation Attack Detection for Face Biometrics
Tsitiridis, A., Conde, C., Gómez-Ayllón, B., & Cabello, E. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00034 · Cited by 5 · see citing works
“…j is given by: The maximum operator is a well-known non-linear biological property exhibited by certain visual cells at low levels of visual cognition that assists in pooling visual inputs from previous layers (Riesenhuber and Poggio, 1999; Lampl et al., 2004) to greater receptive fields.”
BackgroundA deep-learning model-based and data-driven hybrid architecture for image annotation
Wang, Z., Xia, D., & Chang, E. Y. (2010) · doi:10.1145/1878137.1878141 · Cited by 5 · see citing works
“This step corresponds to the operation conducted by cells in V1 and V2 [11], which detect edge signals at the pixel level.”
BackgroundMethodCurvature-based perceptual singularities and texture saliency with early vision mechanisms
Ben-Yosef, G., & Ben‐Shahar, O. (2008) · Journal of the Optical Society of America A · doi:10.1364/josaa.25.001974 · Cited by 4 · see citing works
“For example, the presence of MAX-like behavior was tested experimentally [ 33 ] by comparing neural responses to two bars presented both separately and simultaneously to an anesthetized cat.”
MethodA Study on Visual Perception based Emotion Recognition using Body-Activity Posture
Kim, J.-O. (2011) · The KIPS Transactions PartB · doi:10.3745/kipstb.2011.18b.5.305 · Cited by 4 · see citing works
“시각계 정보처리 단계의 기본 동작인 최대 풀링은 센서 잡음에 대한 위치불변과 강건함을 증대시키며 시각정보 처리를 담당하는 시각피질의 V2/V4영역에서 관측 된다[22, 23].”
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
Akbarinia, A., Rodríguez, R. G., & Párraga, C. A. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.77 · Cited by 4 · see citing works
“Results reported by [33] suggest an inverse relationship between the contrast of a stimulus and the percentage of the signal pooled.”
BackgroundMethodResultInfluentialMisaghian, K., Lugo, J. E., & Faubert, J. (2022) · Biomimetics · doi:10.3390/biomimetics7040193 · Cited by 3 · see citing works
“Utilization of the/a maximum operator in the opponent motion-sensitive neurons simulation is rooted in the discovery of the same sort of computation in the visual cortex of monkeys and cats (Gawne and Martin, 2002; Lampl, Ferster, Poggio, and Riesenhuber, 2004) [25,26].”
BackgroundAn interactive cortical architecture for perceptual organization by accentuation
Domijan, D., & Marić, M. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.028 · Cited by 3 · see citing works
Statistical Principles in Image Modeling
Wu, Y. N., Li, J., Liu, Z., & Zhu, S. C. (2007) · Technometrics · doi:10.1198/004017007000000236 · Cited by 3 · see citing works
“Tolerance of such deformations has been observed in the complex cells in V1 (Lampl et al. 2004; Riesenhuber and Poggio 1999).”
BackgroundMoghaddam, R. F., & Cheriet, M. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2197013 · Cited by 3 · see citing works
“Young children prefer to represent shapes in a metric and holistic way [11]–[13], while adults use a recognition-by-components approach to represent an object as a collection of categorical geometric parts [14].”
BackgroundTRAFFIC SIGN DETECTION BASED ON BIOLOGICALLY VISUAL MECHANISM
Hu, X., Zhu, X., & Li, D. (2012) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xxxix-b3-217-2012 · Cited by 3 · see citing works
Learning Domain-Specific Feature Descriptors for Document Images
Ramakrishnan, K., & Bart, E. (2012) · IAPR International Workshop on Document Analysis Systems (DAS) · doi:10.1109/das.2012.49 · Cited by 3 · see citing works
“These two stages may be stacked several times [15], [16], [13].”
BackgroundUnconscious Biases in Neural Populations Coding Multiple Stimuli
Keemink, S., Tailor, D., & van Rossum, M. C. W. (2018) · Neural Computation · doi:10.1162/neco_a_01130 · Cited by 3 · see citing works
“Furthermore, in other studies evidence for more competitive interaction has been found in area MT (Britten and Heuer, 1999), as well as other visual cortices (Gawne and Martin, 2002; Lampl et al., 2004; Oleksiak et al., 2011).”
BackgroundGiese, M. A., Knoblich, Grosjean, M., Thornton, I. M., & Shiffrar, M. (2005) · MPG.PuRe (Max Planck Society) · Cited by 3 · see citing works
Toward a more biologically plausible model of object recognition
Kouh, M. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 3 · see citing works
Chang, E. Y. (2011) · Springer eBooks · doi:10.1007/978-3-642-20429-6_2 · Cited by 2 · see citing works
Lattice Neural Networks with Spike Trains
Ritter, G. X., & Urcid, G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13803-4_46 · Cited by 2 · see citing works
“These logic operations, speed of computation, as well as work by Poggio and colleagues on the Max operator [6,7] are some of the reasons that we used lattice algebra as the main tool for mathematical modeling.”
Method
- Authors
- Lampl, I., Ferster, D., Poggio, T., & Riesenhuber, M.
- Affiliations
- Weizmann Institute of Science (IL)
- Massachusetts Institute of Technology (US)
- Georgetown University (US)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2004-11
- Volume
- 92
- Issue
- 5
- Pages
- 2704-2713
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2161126623
- Cited by
- 139
- References
- 33
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 3 · 2018: 7 · 2017: 9 · 2016: 11 · 2015: 8 · 2014: 6 · 2013: 11 · 2012: 11
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (R01 EY-04726, R01EY004726)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- complex cells, primary visual cortex, spatial integration, nonlinear summation, membrane potential, receptive fields, object recognition, invariant object recognition, cat visual system
- MeSH terms
- Animals, Cats, Female, Membrane Potentials, Pattern Recognition, Visual, Space Perception, Visual Cortex
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.
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