Full text
|
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview |
Mihaljevic, Bojan and Benavides Piccione, Ruth and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María and Felipe Oroquieta, Javier de (2019). Classification of GABAergic interneurons by leading neuroscientists. "Scientific Data", v. 6 (n. 221); pp. 1-6. ISSN 2052-4463. https://doi.org/10.1038/s41597-019-0246-8.
Title: | Classification of GABAergic interneurons by leading neuroscientists |
---|---|
Author/s: |
|
Item Type: | Article |
Título de Revista/Publicación: | Scientific Data |
Date: | October 2019 |
ISSN: | 2052-4463 |
Volume: | 6 |
Subjects: | |
Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
|
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview |
There is currently no unique catalog of cortical GABAergic interneuron types. In 2013, we asked 48 leading neuroscientists to classify 320 interneurons by inspecting images of their morphology. That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification, showing high agreement for the chandelier and Martinotti types, yet low agreement for most of the remaining types considered. Here we present the dataset containing the classification choices by the neuroscientists according to interneuron type as well as to five prominent morphological features. These data can be used as crisp or soft training labels for learning supervised machine learning interneuron classifiers, while further analyses can try to pinpoint anatomical characteristics that make an interneuron especially difficult or especially easy to classify.
Type | Code | Acronym | Leader | Title |
---|---|---|---|---|
Government of Spain | TIN2016-79684-P | Unspecified | Universidad Politécnica de Madrid | Avances en clasificación multidimensional y detección de anomalías con redes bayesianas |
Horizon 2020 | 785907 | HBP SGA2 | École Polytechnique Fédérale de Lausane | Human Brain Project Specific Grant Agreement 2 |
Item ID: | 63560 |
---|---|
DC Identifier: | http://oa.upm.es/63560/ |
OAI Identifier: | oai:oa.upm.es:63560 |
DOI: | 10.1038/s41597-019-0246-8 |
Official URL: | https://www.nature.com/articles/s41597-019-0246-8 |
Deposited by: | Memoria Investigacion |
Deposited on: | 23 Oct 2020 08:07 |
Last Modified: | 23 Oct 2020 08:58 |