The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Computational Neuroscience
Abstract
The ultimate aim of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information. This goal is not new, but much has changed in the last decade. More is known now about the brain because of advances in neuroscience, more computing power is available for performing realistic simulations of neural systems, and new insights are available from the study of simplifying models of large networks of neurons. Brain models are being used to connect the microscopic level accessible by molecular and cellular techniques with the systems level accessible by the study of behavior.
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Citations
Cites
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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
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Hodgkin, A. L., & Huxley, A. F. (1952) · The Journal of Physiology · doi:10.1113/jphysiol.1952.sp004717 · Cited by 2,491
Computing with Neural Circuits: A Model
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Analog VLSI Implementation of Neural Systems
(1989) · The Kluwer International Series in Engineering and Computer Science · doi:10.1007/978-1-4613-1639-8 · Cited by 1,680
Topography of Cognition: Parallel Distributed Networks in Primate Association Cortex
Goldman-Rakic, P. S. (1988) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.11.030188.001033 · Cited by 1,660
The mechanism of directionally selective units in rabbit's retina.
Barlow, H. B., & Levick, W. R. (1965) · The Journal of Physiology · doi:10.1113/jphysiol.1965.sp007638 · Cited by 1,576
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Cited by
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
Chiel, H. J., & Beer, R. D. (1997) · Trends in Neurosciences · doi:10.1016/s0166-2236(97)01149-1 · Cited by 864
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
Towards a universal taxonomy of macro-scale functional human brain networks
Uddin, L. Q., Yeo, B. T. T., & Spreng, R. N. (2019) · Brain Topography · doi:10.1007/s10548-019-00744-6 · Cited by 683
A canonical microcircuit for neocortex
Douglas, R. J., Martin, K. A. C., & Whitteridge, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.480 · Cited by 514
Metadata
- Authors
- Sejnowski, T. J., Koch, C., & Churchland, P. S.
- Venue
- Science
- Published
- 1988-09-09
- Volume
- 241
- Issue
- 4871
- Pages
- 1299-1306
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4211182857
- Cited by
- 458
- References
- 63
- FWCI
- 20.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sejnowski, T. J., Koch, C., & Churchland, P. S. (1988). Computational Neuroscience. Science. https://doi.org/10.1126/science.3045969
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.
