The DOI matches its Crossref record.
Some Networks That Can Learn, Remember, and Reproduce any Number of Complicated Space-Time Patterns, I
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Citations
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Grossberg, S. (1976) · Biological Cybernetics · doi:10.1007/bf00344744 · Cited by 1,909
Self-citationToward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Neural syntax: cell assemblies, synapsembles, and readers
Buzsáki, G. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.023 · Cited by 1,289
The spatial coding model of visual word identification.
Davis, C. J. (2010) · Psychological Review · doi:10.1037/a0019738 · Cited by 490
Metadata
- Authors
- Grossberg, S.
- Venue
- Indiana University Mathematics Journal
- Published
- 1969
- Volume
- 19
- Issue
- 1
- Pages
- 53-91
- ISSN
- 0022-2518 (ISSN-L)
- OpenAlex
- W2323866847
- Cited by
- 236
- References
- 0
- FWCI
- 7.90
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Some Networks That Can Learn, Remember, and Reproduce any Number of Complicated Space-Time Patterns, I. https://app.cochat.ai/papers/10.1512/iumj.1970.19.19007
- The paper
- Grossberg, S. (1969). Some Networks That Can Learn, Remember, and Reproduce any Number of Complicated Space-Time Patterns, I. Indiana University Mathematics Journal. https://doi.org/10.1512/iumj.1970.19.19007
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.
