The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Neural Representation and the Cortical Code
Abstract
The principle function of the central nervous system is to represent and transform information and thereby mediate appropriate decisions and behaviors. The cerebral cortex is one of the primary seats of the internal representations maintained and used in perception, memory, decision making, motor control, and subjective experience, but the basic coding scheme by which this information is carried and transformed by neurons is not yet fully understood. This article defines and reviews how information is represented in the firing rates and temporal patterns of populations of cortical neurons, with a particular emphasis on how this information mediates behavior and experience.
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
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
Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070 · cited 1×
“…images (Shepard & Metzler 1971), their spatial form (Farah 1989a,b; Farah et al 1988; Kosslyn 1988), and their spatial interactions (Farah 1989a, Hebb 1968) or the affects of spatial attention (Colby 1991, Desimone & Duncan 1995, Kastner et al 1998, Moran & Desimone 1985, Posner & Petersen 1990).”
BackgroundDistributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989) · Nature · doi:10.1038/338334a0 · Cited by 4,183
Thalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564
Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538
On a confusion about a function of consciousness
Block, N. (1995) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00038188 · Cited by 3,364
The somatic marker hypothesis and the possible functions of the prefrontal cortex
Damasio, A. R. (1996) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1996.0125 · Cited by 2,847 · cited 1×
“…representation in individual sensory systems can be found in the literature (Andersen et al 1997, Bertenthal 1996, Bisiach 1993, Colby et al 1995, Damasio 1996, Edelman 1998, Edelman & Duvdevani-Bar 1997, Eggermont 1998, Farah & Feinberg 1997, Fregnac et al 1996, Gallistel 1989, Gross 1992,…”
BackgroundSelective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (1985) · Science · doi:10.1126/science.4023713 · Cited by 2,433
Cited by
Large-scale recording of neuronal ensembles
Buzsáki, G. (2004) · Nature Neuroscience · doi:10.1038/nn1233 · Cited by 1,875
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621
Haider, B., Duque, A., Hasenstaub, A. R., & McCormick, D. A. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.5297-05.2006 · Cited by 962
Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
Hung, C. P., Kreiman, G., Poggio, T., & DiCarlo, J. J. (2005) · Science · doi:10.1126/science.1117593 · Cited by 854
Representation in Cognitive Science
Shea, N. (2018) · doi:10.1093/oso/9780198812883.001.0001 · Cited by 676
Metadata
- Authors
- deCharms, R. C., & Zador, A.
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2000-03
- Volume
- 23
- Issue
- 1
- Pages
- 613-647
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2109387052
- Cited by
- 394
- References
- 27
- FWCI
- 1.12
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neural Representation and the Cortical Code. https://app.cochat.ai/papers/10.1146/annurev.neuro.23.1.613
- The paper
- deCharms, R. C., & Zador, A. (2000). Neural Representation and the Cortical Code. Annual Review of Neuroscience. https://doi.org/10.1146/annurev.neuro.23.1.613
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.
