Atlases and Ontologies



The Allen Human Reference Atlas – 3D, 2020 is a 3-dimensional annotated parcellation of the adult human brain. The atlas includes 141 brain regions spanning the complete volume of the MRI reference brain “ICBM 2009b Nonlinear Symmetric”, a non-linear average of the MNI152 database of 152 normal brain images. These structures include regions from a 2D plate-based histological reference atlas of the adult human brain (Ding et al., 2016), that can be identified in the average MRI volume. The atlas is intended to serve as a positional common coordinate framework for mapping adult human brain data generated across the BICCN. (Allen Human Reference Atlas, 3D, 2020; RRID:SCR_017764)

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Allen Institute for Brain Science (



Enhanced and Unified Anatomical Labeling for a Common Mouse Brain Atlas - To facilitate comparison between existing atlases, the Franklin and Paxinos (FP) label was imported and refined into the Allen Common Coordinate Framework (CCF). Cell type specific transgenic mice and an MRI atlas were used to adjust and further segment the labels. Moreover, new segmentations were created in the dorsal striatum using cortico-striatal connectivity data. The anatomical labels were digitized based on the Allen ontology, and a web-interface was created for easy visualization. These labels provide a resource to isolate and identify mouse brain anatomical structures.

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Related publication: Chon U, Vanselow DJ, Cheng KC, Kim Y. Enhanced and unified anatomical labeling for a common mouse brain atlas. Nat Commun. 2019 Nov 7;10(1):5067. doi: 10.1038/s41467-019-13057-w. PubMed PMID: 31699990; PubMed Central PMCID: PMC6838086.


Yongsoo Kim lab (, Penn State University

 is a curated knowledge base of the neuron types of the rodent hippocampal formation. Knowledge concerning the morphology, electrophysiology, molecular expression, and connectivity of cells in the dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex is distilled from published evidence and is continuously updated as new information becomes available. Each reported neuronal property is documented with a pointer to, and excerpt from, relevant published evidence, such as citation quotes or illustrations. (, RRID:SCR_009023)

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Related publications: a knowledge base of neuron types in the rodent hippocampus. Wheeler DW, White CM, Rees CL, Komendantov AO, Hamilton DJ, Ascoli GA. Elife. 2015 Sep 24;4. pii: e09960. doi: 10.7554/eLife.09960.

Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome. Rees CL, Wheeler DW, Hamilton DJ, White CM, Komendantov AO, Ascoli GA. eNeuro. 2016 Nov 18;3(6). pii: ENEURO.0205-16.2016. eCollection 2016 Nov-Dec.

Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach. Hamilton DJ, White CM, Rees CL, Wheeler DW, Ascoli GA. J Pharm Biomed Anal. 2017 Sep 10;144:269-278. doi: 10.1016/j.jpba.2017.03.062. Epub 2017 Apr 29.

Weighing the Evidence in Peters' Rule: Does Neuronal Morphology Predict Connectivity? Rees CL, Moradi K, Ascoli GA. Trends Neurosci. 2017 Feb;40(2):63-71. doi: 10.1016/j.tins.2016.11.007. Epub 2016 Dec 29. Review.

Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties. Hamilton DJ, Wheeler DW, White CM, Rees CL, Komendantov AO, Bergamino M, Ascoli GA. Brain Inform. 2017 Mar;4(1):1-12. doi: 10.1007/s40708-016-0053-3. Epub 2016 Jun 9.

Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types. Venkadesh S, Komendantov AO, Listopad S, Scott EO, De Jong K, Krichmar JL, Ascoli GA. Front Neuroinform. 2018 Mar 13;12:8. doi: 10.3389/fninf.2018.00008.

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Moradi K, Ascoli GA. Hippocampus. 2019 Aug 31. doi: 10.1002/hipo.23148. [Epub ahead of print]


Giorgio Ascoli lab (, George Mason University




Neuron Phenotype Ontology - An important aspect in the classification of cell type identity and function is the use of powerful and flexible ontologies. The Neuron Phenotype Ontology is a prototype system for managing neuronal phenotypes that spans across the different phases of knowledge discovery. The system comprises a  knowledge base of neuron types and supporting python codes, and supports the concepts of common usage types and evidence based models. It enables researchers to generate a complex neuron phenotype out of any number of individual phenotypes.  Phenotype values are tied to formal biomedical ontologies, ensuring a consistent semantic representation and that the phenotypes themselves can be integrated with other types of data. Version 1.0 of the ontology is currently available. (NIFSTD, RRID:SCR_005414)

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Neuroscience Information Framework (, University of California, San Diego