The DOI matches its Crossref record.
The two-dimensional spatial structure of simple receptive fields in cat striate cortex
Abstract
1. A reverse correlation (6, 8, 25, 35) method is developed that allows quantitative determination of visual receptive-field structure in two spatial dimensions. This method is applied to simple cells in the cat striate cortex. 2. It is demonstrated that the reverse correlation method yields results with several desirable properties, including convergence and reproducibility independent of modest changes in stimulus parameters. 3. In contrast to results obtained with moving stimuli, we find that the bright and dark excitatory subregions in simple receptive fields do not overlap to any great extent. This difference in results may be attributed to confounding the independent variables space and time when using moving stimuli. 4. All simple receptive fields have subregions that vary smoothly in all directions in space. There are no sharp transitions either between excitatory subregions or between subregions and the area surrounding the receptive field. 5. Simple receptive fields vary both in the number of subregions observed, in the elongation of each subregion, and in the overall elongation of the field. In contrast with results obtained using moving stimuli, we find that subregions within a given receptive field need not be the same length. 6. The hypothesis that simple receptive fields can be modeled as either even symmetric or odd symmetric about a central axis is evaluated. This hypothesis is found to be false in general. Most simple receptive fields are neither even symmetric nor odd symmetric. 7. The hypothesis that simple receptive fields can be modeled as the product of a width response profile and an orthogonal length response profile (Cartesian separability) is evaluated. This hypothesis is found to be true for only approximately 50% of the cells in our sample.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Intersubject synchronization of cortical activity during natural vision
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004) · Science · doi:10.1126/science.1089506 · Cited by 1,839
Highly selective receptive fields in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0623-08.2008 · Cited by 1,081
An emergent model of orientation selectivity in cat visual cortical simple cells
Somers, D., Nelson, S., & Sur, M. (1995) · The Journal of Neuroscience · doi:10.1523/jneurosci.15-08-05448.1995 · Cited by 860
Maynard, E. M., Nordhausen, C. T., & Normann, R. A. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(96)95176-0 · Cited by 640
Oblique effect: a neural basis in the visual cortex
Li, B., Peterson, M. R., & Freeman, R. D. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00954.2002 · Cited by 328
Functional 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
Polytrodes: High-Density Silicon Electrode Arrays for Large-Scale Multiunit Recording
Blanche, T. J., Spacek, M. A., Hetke, J. F., & Swindale, N. V. (2005) · Journal of Neurophysiology · doi:10.1152/jn.01023.2004 · Cited by 282
Ponce, C. R., Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., & Livingstone, M. S. (2019) · Cell · doi:10.1016/j.cell.2019.04.005 · Cited by 245
The Emergence of Contrast-Invariant Orientation Tuning in Simple Cells of Cat Visual Cortex
Finn, I. M., Priebe, N. J., & Ferster, D. (2007) · Neuron · doi:10.1016/j.neuron.2007.02.029 · Cited by 248
Local diversity and fine-scale organization of receptive fields in mouse visual cortex
Bonin, V., Histed, M. H., Yurgenson, S., & Reid, R. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2974-11.2011 · Cited by 247
Grubb, M. S., Rossi, F. M., Changeux, J.-P., & Thompson, I. D. (2003) · Neuron · doi:10.1016/s0896-6273(03)00789-x · Cited by 208
Oxytocin mediates early experience-dependent cross-modal plasticity in the sensory cortices
Zheng, J.-J., Li, S.-J., Zhang, X.-D., Miao, W.-Y., Zhang, D., Yao, H., & Yu, X. (2014) · Nature Neuroscience · doi:10.1038/nn.3634 · Cited by 206
Receptive field structure varies with layer in the primary visual cortex
Martinez, L. M., Wang, Q., Reid, R. C., Pillai, C., Alonso, J.-M., Sommer, F. T., & Hirsch, J. A. (2005) · Nature Neuroscience · doi:10.1038/nn1404 · Cited by 203
Functionally distinct inhibitory neurons at the first stage of visual cortical processing
Hirsch, J. A., Martinez, L. M., Pillai, C., Alonso, J.-M., Wang, Q., & Sommer, F. T. (2003) · Nature Neuroscience · doi:10.1038/nn1152 · Cited by 176
Enhancement of visual responsiveness by spontaneous local network activity in vivo
Haider, B., Duque, A., Hasenstaub, A. R., Yu, Y., & McCormick, D. A. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01114.2006 · Cited by 130
Warren, D. J., Fernandez, E., & Normann, R. A. (2001) · Neuroscience · doi:10.1016/s0306-4522(01)00174-9 · Cited by 77
A Current-Mode Hysteretic Winner-take-all Network, with Excitatory and Inhibitory Coupling
Indiveri, G. (2001) · Analog Integrated Circuits and Signal Processing · doi:10.1023/a:1011208127849 · Cited by 75
Cellular mechanisms of temporal sensitivity in visual cortex neurons
Cardin, J. A., Kumbhani, R. D., Contreras, D., & Palmer, L. A. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5279-09.2010 · Cited by 62
Self-citationEstimating average single-neuron visual receptive field sizes by fMRI
Keliris, G. A., Li, Q., Papanikolaou, A., Logothetis, N. K., & Smirnakis, S. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1809612116 · Cited by 48
Deep gabor neural network for automatic detection of mine-like objects in sonar imagery
Thanh Le, H., Phung, S. L., Chapple, P. B., Bouzerdoum, A., Ritz, C. H., & Tran, L. C. (2020) · IEEE Access · doi:10.1109/access.2020.2995390 · Cited by 49
Willmore, B. D. B., Bulstrode, H., & Tolhurst, D. J. (2012) · Vision Research · doi:10.1016/j.visres.2011.12.008 · Cited by 10
Metadata
- Authors
- Jones, J. P., & Palmer, L. A.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1987-12-01
- Volume
- 58
- Issue
- 6
- Pages
- 1187-1211
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W4292676217
- Cited by
- 668
- References
- 0
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- closed
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
