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Responses of Primate Visual Cortical V4 Neurons to Simultaneously Presented Stimuli
Abstract
We report here results from 45 primate V4 visual cortical neurons to the preattentive presentations of seven different patterns located in two separate areas of the same receptive field and to combinations of the patterns in the two locations. For many neurons, we could not determine any clear relationship for the responses to two simultaneous stimuli. However, for a substantial fraction of the neurons we found that the firing rate was well modeled as the maximum firing rate of each stimulus presented separately. It has previously been proposed that taking the maximum of the inputs (“MAX” operator) could be a useful operation for neurons in visual cortex, although there has until now been little direct physiological evidence for this hypothesis. Our results here provide direct support for the hypothesis that the MAX operator plays a significant (although certainly not exclusive) role in generating the receptive field properties of visual cortical neurons.
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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 [27]
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Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,938
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
A Method of Measuring Eye Movemnent Using a Scieral Search Coil in a Magnetic Field
Robinson, D. A. (1963) · IEEE Transactions on Bio-medical Electronics · doi:10.1109/tbmel.1963.4322822 · Cited by 2,234
A method of measuring eye movements using a scleral search coil in a magnetic field
Robinson, D. A. (1963) · Medical Entomology and Zoology · Cited by 1,709
Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex
Luck, S. J., Chelazzi, L., Hillyard, S. A., & Desimone, R. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.1.24 · Cited by 1,615
Implantation of magnetic search coils for measurement of eye position: An improved method
Judge, S. J., Richmond, B. J., & Chu, F. C. (1980) · Vision Research · doi:10.1016/0042-6989(80)90128-5 · Cited by 1,530
Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4
Reynolds, J. H., Chelazzi, L., & Desimone, R. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-05-01736.1999 · Cited by 1,236
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Latency: another potential code for feature binding in striate cortex
Gawne, T. J., Kjær, T. W., & Richmond, B. J. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.2.1356 · Cited by 363
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Metadata
- Authors
- Gawne, T. J., & Martin, J. M.
- Affiliations
- University of Alabama (US)
- ORCID
- Gawne, Timothy J.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2002-09-01
- Volume
- 88
- Issue
- 3
- Pages
- 1128-1135
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2175971006
- Cited by
- 134
- References
- 16
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 6 · 2017: 2 · 2016: 3 · 2015: 5 · 2014: 6 · 2013: 9 · 2012: 13
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r29ey011552-05)
- National Eye Institute (EY-11552-01)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- receptive fields, max operator, firing rate
- MeSH terms
- Animals, Electrophysiology, Macaca mulatta, Models, Neurological, Neurons, Photic Stimulation, Reaction Time, Time Factors, Visual Cortex
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