The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction
Abstract
The fundamental building block of every nervous system is the single neuron. Understanding how these exquisitely structured elements operate is an integral part of the quest to solve the mysteries of the brain. Quantitative mathematical models have proved to be an indispensable tool in pursuing this goal. We review recent advances and examine how single-cell models on five levels of complexity, from black-box approaches to detailed compartmental simulations, address key questions about neural dynamics and signal processing.
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
Hodgkin, A. L., & Huxley, A. F. (1952) · The Journal of Physiology · doi:10.1113/jphysiol.1952.sp004764 · Cited by 23,191
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442
Which Model to Use for Cortical Spiking Neurons?
Izhikevich, E. M. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.832719 · Cited by 2,542
Homeostatic plasticity in the developing nervous system
Turrigiano, G. G., & Nelson, S. B. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1327 · Cited by 2,485
Biophysics of Computation: Information Processing in Single Neurons
Koch, C. (1998) · Oxford University Press eBooks · doi:10.1093/oso/9780195104912.001.0001 · Cited by 2,283
A place theory of sound localization.
Jeffress, L. A. (1948) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0061495 · Cited by 1,480
Estimation of entropy and mutual information
Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
Pattern recognition computation using action potential timing for stimulus representation
Hopfield, J. J. (1995) · Nature · doi:10.1038/376033a0 · Cited by 1,151
London, M., & Häusser, M. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135703 · Cited by 1,149
Information theory and neural coding
Borst, A., & Theunissen, F. E. (1999) · Nature Neuroscience · doi:10.1038/14731 · Cited by 1,113
Cited by
The benefits of noise in neural systems: bridging theory and experiment
McDonnell, M. D., & Ward, L. M. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3061 · Cited by 772
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655
Model of electrical activity in a neuron under magnetic flow effect
Lv, M., Wang, C., Ren, G., Ma, J., & Song, X. (2016) · Nonlinear Dynamics · doi:10.1007/s11071-016-2773-6 · Cited by 513
Active properties of neocortical pyramidal neuron dendrites
Major, G., Larkum, M. E., & Schiller, J. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150343 · Cited by 443
Illuminating dendritic function with computational models
Poirazi, P., & Papoutsi, A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0301-7 · Cited by 292
Metadata
- Authors
- Herz, A. V. M., Gollisch, T., Machens, C. K., & Jaeger, D.
- Venue
- Science
- Published
- 2006-10-06
- Volume
- 314
- Issue
- 5796
- Pages
- 80-85
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2159217888
- Cited by
- 451
- References
- 78
- FWCI
- 7.68
- 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: Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction. https://app.cochat.ai/papers/10.1126/science.1127240
- The paper
- Herz, A. V. M., Gollisch, T., Machens, C. K., & Jaeger, D. (2006). Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction. Science. https://doi.org/10.1126/science.1127240
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.
