Cited by 649Visual perception and processing mechanismsNeural dynamics and brain functionNeuroscience and Neuropharmacology Research

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Summation and Division by Neurons in Primate Visual Cortex

Carandini, Matteo, Heeger, David J.·1994·Science

DOI 10.1126/science.8191289

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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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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-citation
Nonlinear 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
Role of Inhibition in the Specification of Orientation Selectivity of Cells in the Cat Striate Cortex
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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Show all 28 references

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GROSSBERG, S, NEURAL NETWORKS 1: 17 (1988).
Koch, C., Synaptic Function: 637 (1987).

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Background
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BackgroundInfluential
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Background
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Background
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Background
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“…as optimally encoding information as linear systems for both spatial and temporal response properties (McLean and Palmer 1989; DeAngelis et al. 1993; Carandini and Heeger 1994; Olshausen and Field 1996; Bell and Sejnowski 1997; Hurri and Hyvärinen 2003; Boynton and Hegdé 2004; Cai et al. 2004).”
Background
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Learning mechanisms to account for the speed, selectivity and invariance of responses in the visual cortex
Masquelier, T. (2008) · Thèses en ligne de l'Université Toulouse III (Université Toulouse III) · Cited by 1
Studies of cultured neuronal networks using light activated ion channels and pumps
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Going against the Grain: Functionalism and Generalization in Cognitive Science
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Yu, A. J., Giese, M. A., & Poggio, T. (2001) · DSpace@MIT (Massachusetts Institute of Technology)
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Rao, R. P. N. (2007)
System identification of optic-flow processing neurons in the fly: single cell and small circuit models
Weber, F. (2012) · doi:10.5282/edoc.13915
A Population Gain Control Model of Spatiotemporal Responses in the Visual Cortex
Sit, Y. F. (2009) · Texas ScholarWorks (Texas Digital Library)
Hierarchical feed-forward models for robust object recognition
Bax, I. (2007) · PUB – Publications at Bielefeld University (Bielefeld University)
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System level assessment of motor control through patterned microstimulation in the superior colliculus
Katnani, H. A. (2012) · D-Scholarship@Pitt (University of Pittsburgh)
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Rollenhagen, J. E. (2004) · D-Scholarship@Pitt (University of Pittsburgh)
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Kolesnik, M., & Barlit, A. (2015)
Visual Attention and the Role of Normalization
Ni, A. M. (2012) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
UNCERTAINTY AND DECISION-MAKING IN THE HUMAN BRAIN
Vilares, I. (2013) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
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Multi-mode single neuron arithmetics and logics
Zhang, C. N., Zhao, M., & Wang, M. (1999) · IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) · doi:10.1109/pacrim.1999.799567
Cortical simple cells: modeling physiology and image processing applications
Pessoa, L., Heiko Neumann, B., & Littmann, E. (2002) · Workshop on Cybernetic Vision · doi:10.1109/cybvis.1996.629441
A Model of Neuronal Responses in Visual
Mt, A., Simoncelli, E. P., & HEEGERt, D. J. (1998)
MODELING EARLY VISION: PROBABILISTIC COMPUTATION USING SPIKING NEURONS, POPULATION CODES, AND CUDA
Coates, D. R. (2009)
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Hillman, C. C., & Narayanan, S. N. (2007)
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“First, we seek a physiologically interpretable model based on plausible neural operations including multiplicative modulation and local normalization through divisive inhibition [5, 21].”
Method
The study of neural oscillations by traversing scales in the brain
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Physical channel modeling by calcium signaling in molecular communication based nanonetwork
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Background
Characterizing surround suppression in motion direction perception
Kurt, A. G. (2021) · Bilkent University Institutional Repository (Bilkent University)
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Authors: Carandini, M., & Heeger, D. J.
Affiliations: New York University (US)
ORCID: Carandini, Matteo
Heeger, David J.
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 1994-05-27
Volume: 264
Issue: 5163
Pages: 1333-1336
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.8191289
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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Summation and Division by Neurons in Primate Visual Cortex

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