The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Binary Spiking in Auditory Cortex
Open accessAbstract
Neurons are often assumed to operate in a highly unreliable manner: a neuron can signal the same stimulus with a variable number of action potentials. However, much of the experimental evidence supporting this view was obtained in the visual cortex. We have, therefore, assessed trial-to-trial variability in the auditory cortex of the rat. To ensure single-unit isolation, we used cell-attached recording. Tone-evoked responses were usually transient, often consisting of, on average, only a single spike per stimulus. Surprisingly, the majority of responses were not just transient, but were also binary, consisting of 0 or 1 action potentials, but not more, in response to each stimulus; several dramatic examples consisted of exactly one spike on 100% of trials, with no trial-to-trial variability in spike count. The variability of such binary responses differs from comparably transient responses recorded in visual cortical areas such as area MT, and represent the lowest trial-to-trial variability mathematically possible for responses of a given firing rate. Our study thus establishes for the first time that transient responses in auditory cortex can be described as a binary process, rather than as a highly variable Poisson process. These results demonstrate that cortical architecture can support a more precise control of spike number than was previously recognized, and they suggest a re-evaluation of models of cortical processing that assume noisiness to be an inevitable feature of cortical codes.
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
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237
Chung, S., Li, X., & Nelson, S. B. (2002) · Neuron · doi:10.1016/s0896-6273(02)00659-1 · Cited by 530 · cited 1×
“8); such longer-lasting suppression may be attributable, at least in part, to short-term synaptic depression (Chung et al., 2002).”
BackgroundDynamic stochastic synapses as computational units.
Maass, W., & Zador, A. M. (1999) · Neural Computation · doi:10.1162/089976699300016494 · Cited by 149
Self-citation
Cited by
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
Sparse coding of sensory inputs
Olshausen, B. A., & Field, D. J. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.07.007 · Cited by 1,404
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
A synaptic memory trace for cortical receptive field plasticity
Froemke, R. C., Merzenich, M. M., & Schreiner, C. E. (2007) · Nature · doi:10.1038/nature06289 · Cited by 600
Somatosensory integration controlled by dynamic thalamocortical feed-forward inhibition
Gabernet, L., Jadhav, S. P., Feldman, D. E., Carandini, M., & Scanziani, M. (2005) · Neuron · doi:10.1016/j.neuron.2005.09.022 · Cited by 599
Metadata
- Authors
- DeWeese, M. R., Wehr, M., & Zador, A. M.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2003-08-27
- Volume
- 23
- Issue
- 21
- Pages
- 7940-7949
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2160482844
- Cited by
- 355
- References
- 4
- FWCI
- 9.22
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Binary Spiking in Auditory Cortex. https://app.cochat.ai/papers/10.1523/jneurosci.23-21-07940.2003
- The paper
- DeWeese, M. R., Wehr, M., & Zador, A. M. (2003). Binary Spiking in Auditory Cortex. The Journal of Neuroscience. https://doi.org/10.1523/jneurosci.23-21-07940.2003
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.