The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
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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“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]
Citations
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“It is also possible that differences in integration properties at low compared to high contrasts are related to the well-known stimulusdependent level of saturation of V1 neurons that was suggested to arise from a contrast normalization mechanism (Carandini and Heeger 1994; Heeger 1992).”
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Items 101–138 of 138
A robust scene descriptor based on largest singular values for cortex-like mechanisms
Hormat, A. M., Rostami, V., & Menhaj, M. B. (2013) · Iranian Conference on Fuzzy Systems · doi:10.1109/ifsc.2013.6675585 · Cited by 2
“For instance, operations at the complex cells are similar to some behaviors of complex cells in V1 [15] and V4 [16].”
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“…219 where the response to two simultaneously presented stimuli is the same as the response to the 220 single most effective stimulus (Heuer and Britten, 2002; Lampl et al. 2004; Sato 1989; Rolls et al 221 2003; Oleksiak et al. 2011; Finn and Ferster 2007; Gawne and Martin, 2002b; Gawne 2008).”
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“…to be similar in magnitude to the larger of the responses to the two stimuli presented separately, a winner-take-all or “MAX” result, at least for highcontrast stimuli (Sato, 1989; Heuer and Britten, 2002; Rolls et al., 2003; Lampl et al., 2004; Finn and Ferster, 2007; Oleksiak et al., 2011).”
Ship Detection Algorithm Based on Visual Cognition
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“These units receive responses from S1 units and perform hierarchy of pooling operations [44] to linear feature integration.”
BackgroundHybrid Gray-Scale and Fuzzy Morphological/Linear Perceptrons Trained By Extreme Learning Machine
Sussner, P., Campiott, I., & Torres, M. A. Q. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206886 · Cited by 1
“In addition, neurons with dendrites can not only implement a rich repertoire of logical functions but also other lattice-based operations such as the maximum [18], [19], [20].”
BackgroundHigh-level visual object representations in inferior temporal cortex
Mur, M. (2011) · Research Publications (Maastricht University) · doi:10.26481/dis.20111202mm · Cited by 1
Cortical Networks of Visual Recognition
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Multiclass object recognition inspired by the ventral visual pathway
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“Support for a MAX operation in at least some cells in visual cortex has been found in physiological studies [18].”
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“The V4 neuron (also known as complex cell [23]) in the visual cortex computes the maximum value among its input activities [24].”
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“Intracellular recordings from complex cells demonstrated strong sublinearities of summation of synaptic potentials from oriented bars presented within the RF (Lampl et al., 2004).”
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Neural computation in small sensory systems
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Spatial information processing in humans and monkeys
Oleksiak, A. (2005) · Utrecht University Repository (Utrecht University)
A combined coding scheme for visual stimuli
Roth, S. (2008) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-a-005824009
“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 (Lampl et al., 2004).”
BackgroundThe Gabor-Einstein Wavelet: A Model for the Receptive Fields of V1 to MT NeuronsPreprint
Odaibo, S. G. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.5589
“ich they are summed to yield the MT response (Adelson and Bergen, 1985, 1986). Intra-processing is modication of the input responses during the combination process. For example, the Soft MAX scheme (Lampl, Ferster, Poggio, and Riesenhuber, 2004; Riesenhuber and Poggio, 1999; Nowlan and Sejnowski, 1995). Post-processin…”
BackgroundOptimal information encoding for multiple, simultaneously presented stimuli
Pieczkowski, J., York, L., Kotaleski, J. H., & van Rossum, M. C. W. (2012) · BMC Neuroscience · doi:10.1186/1471-2202-13-s1-p17
“[5], [6], [7]) suggest a non-linear encoding scheme using a maximum rule, where the response of a single neuron to a pair of stimuli equals the response to the constituent that on its own produces the maximum response, i.”
BackgroundPeyravi, N., & Jafari, S. (2010) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2010.5518565
a winner-take-all rule Spatial summation in macaque parietal area 7a follows
Oleksiak, A., Klink, P. C., Postma, A., & van der Ham, I. J. M. (2011)
LEARNING AND ADAPTATION IN COMPUTER VISION
Telematica, E., & Orabona, F. (2007)
Fast Categorisation of Articulated Human Motion
Schindler, K., & Gool, L. V. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-900-7.ch009
“The MAX is also consistent with electro-physiological measurements (Gawne and Martin, 2002; Lampl et al., 2004).”
ResultA hardware accelerated object recognition system using event-based image sensor
Ding, R. (2015) · doi:10.32657/10356/65374
“Bio-plausible implementations of the MAX operation were proposed in [81], and biological evidences of neurons displaying MAX-like behavior have been found in a subclass of complex cells in V 1 [122] and cells in V 4 [123].”
MethodBiases in multivariate neural population codesPreprint
Keemink, S., & van Rossum, M. C. W. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/113803
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BackgroundTexture Feature Extraction Inspired By Natural Vision System And Hmax Algorithm
Madanian, M., Vafaei, A., & Monadjemi, S. A. (2012) · Journal of Mathematics and Computer Science · doi:10.22436/jmcs.04.02.09
(2014) · Society for Neuroscience eBooks · doi:10.1523/hon.008009
Misaghian, K., Lugo, J. E., & Faubert, J. (2024) · Biomimetics · doi:10.3390/biomimetics9010027
Flexible integration of natural stimuli by auditory cortical neurons
Ang, G. W. Y., Clopath, C., & Kozlov, A. S. (2026) · Journal of Neurophysiology · doi:10.1152/jn.00200.2025
“The AND operation, by constructing more refined representations that increase selectivity, would facilitate the discrimination of one auditory stimulus from another (e.g., distinguishing "cat" from "hat").”
Impact of synaptic depression on network activity and implications for neural coding
York, L. (2011) · ERA
Metadata
- 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
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