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Cited by 898

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Spatial learning with a minislab in the dorsal hippocampus.

Open access
Moser, M B, Moser, E I, Forrest, E, Andersen, P, Morris, R G·1995·Proceedings of the National Academy of Sciences

Abstract

We have determined the volume and location of hippocampal tissue required for normal acquisition of a spatial memory task. Ibotenic acid was used to make bilateral symmetric lesions of 20-100% of hippocampal volume. Even a small transverse block (minislab) of the hippocampus (down to 26% of the total) could support spatial learning in a water maze, provided it was at the septal (dorsal) pole of the hippocampus. Lesions of the septal pole, leaving 60% of the hippocampi intact, caused a learning deficit, although normal electrophysiological responses, synaptic plasticity, and preserved acetylcholinesterase staining argue for adequate function of the remaining tissue. Thus, with an otherwise normal brain, hippocampal-dependent spatial learning only requires a minislab of dorsal hippocampal tissue.

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Crossref · 10.1073/pnas.92.21.9697

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Metadata

Authors
Moser, M. B., Moser, E. I., Forrest, E., Andersen, P., & Morris, R. G.
Venue
Proceedings of the National Academy of Sciences
Published
1995-10-10
Volume
92
Issue
21
Pages
9697-9701
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2092764200
Cited by
898
References
4
FWCI
6.85
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Spatial learning with a minislab in the dorsal hippocampus. https://app.cochat.ai/papers/10.1073/pnas.92.21.9697
The paper
Moser, M. B., Moser, E. I., Forrest, E., Andersen, P., & Morris, R. G. (1995). Spatial learning with a minislab in the dorsal hippocampus. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.92.21.9697

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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.