The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
New insights into the classification and nomenclature of cortical GABAergic interneurons
Open accessAbstract
A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Chandelier INs, also known as axo-axonic neurons, are the most recognizable INs in terms of morphology (DeFelipe et al., 2013) due to the unique candlestick-like synaptic terminal arrays they form to specifically target the axon initial segment of pyramidal cells.”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as DeFelipe et al., 2013
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“an attempt to produce a systematic classification of neocortical INs based on morphological features alone by a large number of investigators showed that there are too many ambiguities for “experts” to agree on the identity of most IN subtypes when strictly relying on morphological data”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as DeFelipe et al., 2013
In the citing paper
“the definition of a cell type in the brain remains controversial20,21”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [21]
In the citing paper
“the field has not readily converged on a consensus number, or even definition, of neuronal cell types”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as DeFelipe et al., 2013
In the citing paper
“Using whole-cell recordings with morphological recovery, we identified one major excitatory and seven inhibitory types of neurons in L4 of adult mouse visual cortex (V1).”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [41]
In the citing paper
“A century after Ramon y Cajal described ‘short axon cells’, rigorous, quantitative and scalable analysis of interneuron morphology remains difficult11.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [11]
In the citing paper
“Consequently, the issue of interneuron diversity remains contentious11.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [11]
In the citing paper
“Similarly, morphological variations between neurons of the same class may seem continuous and intractable11 but may belie the more specific and discrete connectivity patterns between certain presynaptic and postsynaptic elements56,67.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Statistical pattern recognition: a review
Jain, A. K., Duin, P., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,898
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,758
Probabilistic Reasoning in Intelligent Systems
(1988) · Elsevier eBooks · doi:10.1016/c2009-0-27609-4 · Cited by 3,371
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., & Moy, L. P. (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859894 · Cited by 1,209
(2006) · doi:10.1093/acprof:oso/9780195100273.001.0001 · Cited by 1,186
Microstructure of the neocortex: comparative aspects
DeFelipe, J., Alonso-Nanclares, L., & Arellano, J. I. (2002) · Journal of Neurocytology · doi:10.1023/a:1024130211265 · Cited by 686
Self-citationNeuroMorpho.Org: A Central Resource for Neuronal Morphologies
Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2055-07.2007 · Cited by 664
Self-citationPerceptual Neuroscience: The Cerebral Cortex
Mountcastle, V. B. (1998) · Cited by 476
Research works printed but not linked: 5
- Jackson, J. H. On classification and on methods of investigation (1874) in Selected Writings (ed. Taylor, E., Hodder and Stoughton, 1931).
- von Kölliker, A. Handbuch der Gewebelehre des Menschen. (Engelmann, 1852) (in German).
- Berlin, R. Beitrag zur Strukturlehre der Grosshirnwindungen (Junge, 1858) (in German).
- Golgi, C. Sulla struttura della sostanza grigia del cervello (Comunicazione preventiva). Gazz. Med. Ital. Lombardia 33, 244–246 (1873) (in Italian).
- Cajal, S. R. Nuevo concepto de la histología de los centros nerviosos. Revista de Ciencias Médicas de Barcelona 18, 361–376 (1892); English translation available in DeFelipe, J. & Jones, E. G. Cajal on the Cerebral Cortex (Oxford Univ. Press, 1988).
Cited by
Sorted by citations
Items 501–600 of 836
Feature selection for the classification of traced neurons
López-Cabrera, J. D., & Lorenzo‐Ginori, J. V. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.04.002 · Cited by 11
Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification
Ofer, N., Shefi, O., & Yaari, G. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09466-8 · Cited by 10
Navarro, D., Alvarado-Olivarez, M., Figueroa, A., Gonzalez-Liencres, C., Salas-Lucia, F., Pacheco, P., Sanchez-Vives, M. V., & Berbel, P. J. (2019) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2019.00054 · Cited by 11
Structural and Functional Properties of Hippocampal Neurons
Spruston, N., Piskorowski, R. A., Chevaleyre, V., Eyre, M. D., McBain, C. J., & Bartos, M. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780190065324.003.0005 · Cited by 11
Self-citationGamlin, C. R., Schneider-Mizell, C. M., Mallory, M., Elabbady, L., Gouwens, N. W., Williams, G., Mukora, A., Dalley, R. A., Bodor, Á. L., Brittain, D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.22.533857 · Cited by 11
Haikonen, J., Szrinivasan, R., Ojanen, S., Rhee, J. K., Ryazantseva, M., Sulku, J., Zumaraite, G., & Lauri, S. E. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02641-2 · Cited by 9
Zhang, X., Wu, M., Cheng, L., Cao, W., Liu, Z., Yang, S., & Kim, M. S. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02756-6 · Cited by 13
Feng, X., Gao, Y., Chu, F., Shan, Y., Liu, M., Wang, Y.-Y., Zhu, Y., Lu, Q. R., & Li, M. (2025) · Nature Communications · doi:10.1038/s41467-025-56058-8 · Cited by 10
Self-citationSzilágyi, T., Száva, I., Metz, J., Mihály, I., & Orbán-Kis, K. (2014) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2014.08.004 · Cited by 10
López-Cabrera, J. D., Hernández-Pérez, L. A., Orozco‐Morales, R., & Lorenzo‐Ginori, J. V. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108835 · Cited by 10
Hill, R., Grech, A. M., Notaras, M. J., Sepulveda, M., & van den Buuse, M. (2020) · Neurobiology of Stress · doi:10.1016/j.ynstr.2020.100253 · Cited by 10
Barros, M. T., Šiljak, H., Mullen, P., Papadias, C. B., Hyttinen, J., & Marchetti, N. (2022) · Molecules · doi:10.3390/molecules27196256 · Cited by 10
Building blocks of the cerebral cortex: from development to the dish
Harris, J. M., Tomassy, G. S., & Arlotta, P. (2015) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.192 · Cited by 15
Rolaki, A., Pistollato, F., Munn, S., & Bal‐Price, A. (2019) · OECD series on adverse outcome pathways · doi:10.1787/7ca86a34-en · Cited by 12
ARX regulates cortical interneuron differentiation and migrationPreprint
Lim, Y., Akula, S. K., Myers, A. K., Chen, C., Rafael, K. A., Walsh, C. A., Golden, J. A., & Cho, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.31.578282 · Cited by 11
Conservation, alteration, and redistribution of mammalian striatal interneuronsPreprint
Corrigan, E. K., DeBerardine, M., Poddar, A., Garcίa, M. T., Schmitz, M. T., Harwell, C. C., Paredes, M. F., Krienen, F. M., & Pollen, A. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.29.605664 · Cited by 10
Guan, L., Qiu, M., Li, N., Zhou, Z., Ye, R., Zhong, L., Xu, Y., Ren, J., Liang, Y., Shao, X., et al. (2024) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-24-00429 · Cited by 9
Synaptic Microcircuits in the Barrel Cortex
Radnikow, G., Qi, G., & Feldmeyer, D. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2975-7_4 · Cited by 9
Self-citationLinde, J., & Zimmer‐Bensch, G. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.586133 · Cited by 11
Vandevelde, J. R., Yang, J., Albrecht, S., Lam, H., Kaufmann, P. M., Luhmann, H. J., & Stüttgen, M. C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac141 · Cited by 9
Deciphering the cellular heterogeneity of the insect brain with single‐cell RNA sequencing
Wang, X., Zhai, Y., & Zheng, H. (2023) · Insect Science · doi:10.1111/1744-7917.13270 · Cited by 9
Development and Developmental Disorders of the Cerebral Cortex
ten Donkelaar, H. J., Lammens, M. M. Y., Aronica, E., van Bokhoven, H., Ulzen, K. K.-V., & Hori, A. (2014) · Springer eBooks · doi:10.1007/978-3-642-54687-7_10 · Cited by 9
Challenges in Retinal Circuit Regeneration
Osakada, F., & Takahashi, M. (2015) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.b14-00771 · Cited by 9
Interneuron diversity in the rat dentate gyrus: An unbiased in vitro classification
Degro, C. E., Bolduan, F., Vida, I., & Booker, S. A. (2022) · Hippocampus · doi:10.1002/hipo.23408 · Cited by 11
Mehta, K., Goldin, R., & Ascoli, G. A. (2022) · Network Neuroscience · doi:10.1162/netn_a_00283 · Cited by 9
Self-citationJacobs, B., Lee, L. J., Schall, M., Raghanti, M. A., Lewandowski, A. H., Kottwitz, J. J., Roberts, J. F., Hof, P. R., & Sherwood, C. C. (2015) · The Journal of Comparative Neurology · doi:10.1002/cne.23841 · Cited by 9
Self-citationFreire, M. A. M., Faber, J., Lemos, N. A. M., dos Santos, J. R., Cavalcanti, P. F., Lima, R. H., & Morya, E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130354 · Cited by 10
Evolutionary Specializations of Human Brain Microstructure
Sherwood, C. C., Bauernfeind, A. L., Verendeev, A., Raghanti, M. A., & Hof, P. R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00127-5 · Cited by 9
Self-citationKoerling, A.-L., Fuchsberger, T., Paulsen, O., & Hay, Y. A. (2018) · Neuropharmacology · doi:10.1016/j.neuropharm.2018.11.032 · Cited by 8
Neuroinformatics for Degenerate Brains
Schutter, E. D. (2015) · Neuroinformatics · doi:10.1007/s12021-015-9294-1 · Cited by 9
Quantifying topological invariants of neuronal morphologiesPreprint
Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J. C., Hess, K. P., & Markram, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08432 · Cited by 9
Self-citationFunctional consequences of pre- and postsynaptic expression of synaptic plasticityPreprint
Costa, R. P., Mizusaki, B. E. P., Sjöström, P. J., & van Rossum, M. C. W. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/075317 · Cited by 9
Voltage-Gated Na+ Channels in Alzheimer’s Disease: Physiological Roles and Therapeutic Potential
Baumgartner, T. J., Haghighijoo, Z., Goode, N. A., Dvorak, N. M., Arman, P., & Laezza, F. (2023) · Life · doi:10.3390/life13081655 · Cited by 10
Topology recapitulates morphogenesis of neuronal dendrites
Liao, M., Bird, A. D., Cuntz, H., & Howard, J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113268 · Cited by 8
The production and functions of caudal ganglionic eminence-derived inhibitory neurons
Horton, E. C., & Paredes, M. F. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2025.06.007 · Cited by 9
Ostenrath, A. M., Faturos, N., Çobanoğlu, Y. I. Ç., Serneels, B., Jeong, I., Dayanc, E. D., Enz, A., Hinrichsen, F., Mutlu, A. K., Bardenhewer, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62115-z · Cited by 8
Kanari, L., Schmidt, S., Casalegno, F., Delattre, É., Lukic, J. B., Negrello, T., Shi, Y., Meystre, J., Defferrard, M., Schürmann, F., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-71217-7 · Cited by 8
Self-citationFeng, J., Lu, W., Wang, G., Zhao-ming, Z., Sun, Y., Du, K., & Wang, J. (2018) · Oncotarget · doi:10.18632/oncotarget.25740 · Cited by 8
Fluorescent transgenic mouse models for whole-brain imaging in health and disease
Arias, A., Manubens-Gil, L., & Dierssen, M. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.958222 · Cited by 9
Woo, S., Kim, Y. R., Bak, M. S., Chung, G., Kim, S. J., & Kim, S. K. (2022) · Experimental Neurobiology · doi:10.5607/en22029 · Cited by 8
Munch, A. S., Amat-Foraster, M., Agerskov, C., Bastlund, J. F., Herrik, K. F., & Richter, U. (2023) · Journal of Psychopharmacology · doi:10.1177/02698811231164231 · Cited by 8
Potential Roles for the GluN2D NMDA Receptor Subunit in Schizophrenia
Vinnakota, C., Hudson, M. R., Jones, N. C., Sundram, S., & Hill, R. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411835 · Cited by 7
Peng, Y., Yang, H., Xue, Y.-H., Chen, Q., Jin, H., Liu, S., Yao, S., & Du, M. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1209302 · Cited by 8
Chofflet, N., Naito, Y., Pastore, A. J., Padmanabhan, N., Nguyen, P. T., Poitras, C., Feller, B., Yi, N., Prooijen, J. V., Khaled, H., et al. (2024) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2024.1371145 · Cited by 9
Multiple Patterns of Axonal Collateralization of Single Layer III Neurons of the Rat Presubiculum
Honda, Y., & Furuta, T. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2019.00045 · Cited by 8
Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions
Jiang, S., Pan, Z., Feng, Z., Guan, Y., Ren, M., Ding, Z., Chen, S., Gong, H., Luo, Q., & Li, A. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03714-z · Cited by 8
Wang, Y., Yin, X., He, X., Zhou, C.-M., Shen, J.-C., & Tong, J. (2021) · Aging · doi:10.18632/aging.202684 · Cited by 8
Self-citationGutman-Wei, A. Y., & Brown, S. P. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.728832 · Cited by 9
Stewart, M., Morello, T. D., Kollmar, R., & Orman, R. (2021) · Biomedicines · doi:10.3390/biomedicines9101454 · Cited by 8
Marghi, Y. M., Gala, R., Baftizadeh, F., & Sümbül, U. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00683-8 · Cited by 7
Reconstructing the brain: from image stacks to neuron synthesis
Shillcock, J. C., Hawrylycz, M., Hill, S., & Peng, H. (2016) · Brain Informatics · doi:10.1007/s40708-016-0041-7 · Cited by 8
Self-citationMartens, M. B., Houweling, A. R., & Tiesinga, P. H. E. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0629-1 · Cited by 8
Measurements of neuronal morphological variation across the rat neocortex
Friedman, R. (2020) · Neuroscience Letters · doi:10.1016/j.neulet.2020.135077 · Cited by 8
Minimizing shrinkage of acute brain slices using metal spacers during histological embedding
Bolduan, F., Grosser, S., & Vida, I. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02141-3 · Cited by 8
Chizhov, A. V., & Merkulyeva, N. S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008333 · Cited by 8
Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit
Miyashita, Y. (2024) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-081623-091311 · Cited by 9
Activity-Dependent Ectopic Spiking in Parvalbumin-Expressing Interneurons of the Neocortex
Theyel, B. B., Stevenson, R. J., & Connors, B. W. (2024) · eNeuro · doi:10.1523/eneuro.0314-23.2024 · Cited by 7
Species Differences in the Organization of the Ventral Cochlear Nucleus
Baizer, J. S., Wong, K. M., Salvi, R., Manohar, S., Sherwood, C. C., Hof, P. R., Baker, J. F., & Witelson, S. F. (2017) · The Anatomical Record · doi:10.1002/ar.23751 · Cited by 7
Self-citationHsing, H.-W., Zhuang, Z.-H., Niou, Z., & Chou, S. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz291 · Cited by 8
Deng, R., Kao, J. P. Y., & Kanold, P. O. (2021) · Scientific Reports · doi:10.1038/s41598-021-82679-2 · Cited by 7
Hewitt, L. T., Ordemann, G. J., & Brager, D. H. (2021) · Physiological Reports · doi:10.14814/phy2.14848 · Cited by 7
Neuronal classification from network connectivity via adjacency spectral embedding
Mehta, K., Goldin, R., Marchette, D. J., Vogelstein, J. T., Priebe, C. E., & Ascoli, G. A. (2021) · Network Neuroscience · doi:10.1162/netn_a_00195 · Cited by 7
Self-citationComputational Concepts for Reconstructing and Simulating Brain Tissue
Schürmann, F., Courcol, J.-D., & Ramaswamy, S. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_10 · Cited by 7
Qin, L., Liang, X., Qi, Y., Luo, Y., Xiao, Q., Huang, D., Zhou, C., Jiang, L. H., Zhou, M., Zhou, Y., et al. (2023) · Neuropharmacology · doi:10.1016/j.neuropharm.2023.109669 · Cited by 7
Hippocampal parvalbumin and perineuronal nets: Possible involvement in anxiety‐like behavior in rats
Fan, Z., Gong, X., Xu, H., Qu, Y., Li, B., Li, L., Yan, Y., Wu, L., & Yan, C. (2023) · Hippocampus · doi:10.1002/hipo.23595 · Cited by 7
The Minicolumn in Comparative Context
Spocter, M. A., Raghanti, M. A., Butti, C., Hof, P. R., & Sherwood, C. C. (2015) · Springer eBooks · doi:10.1007/978-94-017-9900-3_5 · Cited by 7
Self-citationOrbán-Kis, K., Szabadi, T., & Szilágyi, T. (2015) · PubMed · Cited by 7
Reyes, L. D., Harland, T., Reep, R. L., Sherwood, C. C., & Jacobs, B. (2016) · Brain Behavior and Evolution · doi:10.1159/000445495 · Cited by 7
Self-citationJiménez, S., López, J. M., Lozano, D. D., Morona, R., González, A., & Moreno, N. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02123-5 · Cited by 7
Morello, T. D., Kollmar, R., Ramzaoui, A., Stewart, M., & Orman, R. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02459-0 · Cited by 7
Laturnus, S., & Berens, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.14.448271 · Cited by 7
Olfactory deficits in aging and Alzheimer’s—spotlight on inhibitory interneurons
Elhabbari, K., Sireci, S., Rothermel, M., & Brunert, D. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1503069 · Cited by 7
Sparks, H. D., Cross, K. A., Choi, J. W., Courellis, H. S., Thum, J. A., Koenig, E., & Pouratian, N. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.11.077 · Cited by 6
Blažetić, S., Krajina, V., Labak, I., Viljetić, B., Pavić, V., Ivić, V., Balog, M., Schnaar, R. L., & Heffer, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242417218 · Cited by 6
Espinoza, F., Carrazana‐Escalona, R., Retamal-Fredes, E., Ávila, D., Papes, F., Muotri, A. R., & Ávila, A. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167178 · Cited by 5
Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review
Leite, J., Nhoatto, F., Jacob, A. C., Santana, R., & Lobato, F. M. F. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09674-6 · Cited by 6
Distribution of calbindin-positive neurons across areas and layers of the marmoset cerebral cortex
Atapour, N., Rosa, M. G. P., Bai, S., Bednarek, S., Kulesza, A., Saworska, G., Teymornejad, S., Worthy, K. H., & Majka, P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012428 · Cited by 8
Long‐range inputome of prefrontal GABAergic interneurons in the Alzheimer's disease mouse
Ren, M., Li, Y., Jiang, T., Liu, B., Li, X., Jia, X., Li, A., Luo, Q., Gong, H., & Li, X. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14552 · Cited by 5
Harris, A. C., Jin, X., Greer, J. E., Povlishock, J. T., & Jacobs, K. M. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105801 · Cited by 8
Zhu, J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112904 · Cited by 6
Ostos, S., Aparicio, G., Fernaud-Espinosa, I., DeFelipe, J., & Muñoz, A. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac314 · Cited by 7
Self-citationSingle Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
Devienne, G. F., Gac, B. L., Piquet, J., & Cauli, B. (2018) · Journal of Visualized Experiments · doi:10.3791/57627-v · Cited by 6
Self-citationA systematic evaluation of interneuron morphology representations for cell type discriminationPreprint
Laturnus, S., Kobak, D., & Berens, P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/591370 · Cited by 6
Costantini, I., Morgan, L., Yang, J., Balbastre, Y., Varadarajan, D., Pesce, L., Scardigli, M., Mazzamuto, G., Gavryusev, V., Castelli, F., et al. (2021) · bioRxiv · doi:10.1101/2021.10.20.464979 · Cited by 4
Self-citationExpression of substance P, NPY and their Receptors Is Altered in Major DepressionPreprint
Barde, S. S., Benitez, J. A., Zhong, W., Solarz, A., Mei, I., Prud’homme, J., Palkovits, M., Turecki, G., Mulder, J., Uhlén, M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.14.516867 · Cited by 6
Anti‐Glutamic Acid Decarboxylase 65 (GAD65) Antibodies and Neurological Syndromes
Mohammadzadeh, O., Fahimzadeh, N., Elhami, F., & Shobeiri, S. S. (2025) · The Journal of Gene Medicine · doi:10.1002/jgm.70039 · Cited by 5
Barcodes distinguishing morphology of neuronal tauopathy
Beers, D., Goniotaki, D., Hanger, D. P., Goriely, A., & Harrington, H. A. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043006 · Cited by 7
Takács, V. T., Papp, P., Orosz, Á., Bardóczi, Z., Zsoldos, T., Zichó, K., Watanabe, M., Maglóczky, Z., Gombás, P., Freund, T. F., et al. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0372-24.2024 · Cited by 6
Self-citationHippocampal architecture viewed through the eyes of methodological development
Zhao, L., & Palomero‐Gallagher, N. (2025) · Anatomical Science International · doi:10.1007/s12565-025-00878-7 · Cited by 8
Microcircuits of the Neocortex
Frӧhlich, F. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801560-5.00007-0 · Cited by 5
Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases
Zhang, X., Lü, P., & Shen, X. (2022) · Clinical and Translational Discovery · doi:10.1002/ctd2.95 · Cited by 6
ZHANG, Z.-Y., ZHONG, W.-X., ZHANG, K.-L., & Li, M. (2022) · 世界针灸杂志:英文版 · doi:10.1016/j.wjam.2022.09.003 · Cited by 5
Kiritani, T., Pala, A., Gasselin, C., Crochet, S., & Petersen, C. C. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287174 · Cited by 5
Wheeler, D. W., Banduri, S., Sankararaman, S., Vinay, S., & Ascoli, G. A. (2024) · Nature Communications · doi:10.1038/s41467-024-45741-x · Cited by 5
Self-citationAdams, D. L., Economides, J. R., & Horton, J. C. (2015) · Visual Neuroscience · doi:10.1017/s0952523815000218 · Cited by 5
Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks
van Gils, T., Tiesinga, P. H. E., Englitz, B., & Martens, M. B. (2019) · Neural Computation · doi:10.1162/neco_a_01215 · Cited by 4
Zhang, Q., Zeng, Y., & Yang, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-61647-2 · Cited by 5
Function and Evolution of the Reptilian Cerebral Cortex
Naumann, R. K., & Laurent, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820584-6.00009-x · Cited by 6
Transcriptional Regulation of Cortical Interneuron Development
Pai, E. L.-L., Vogt, D. L., Hu, J. S., & Rubenstein, J. L. R. (2024) · Oxford University Press eBooks · doi:10.1093/med/9780197549469.003.0047 · Cited by 6
Self-citationRiedemann, S., Sutor, B., Bergami, M., & Riedemann, T. (2019) · The Journal of Comparative Neurology · doi:10.1002/cne.24673 · Cited by 5
Metadata
- Authors
- DeFelipe, J., López-Cruz, P. L., Benavides-Piccione, R., Bielza, C., Larrañaga, P., Anderson, S., Burkhalter, A., Cauli, B., Fairén, A., Feldmeyer, D., Fishell, G., Fitzpatrick, D., Freund, T. F., González-Burgos, G., Hestrin, S., Hill, S., Hof, P. R., Huang, J., Jones, E. G., Kawaguchi, Y., Kisvárday, Z., Kubota, Y., Lewis, D. A., Marín, O., Markram, H., McBain, C. J., Meyer, H. S., Monyer, H., Nelson, S. B., Rockland, K., Rossier, J., Rubenstein, J. L. R., Rudy, B., Scanziani, M., Shepherd, G. M., Sherwood, C. C., Staiger, J. F., Tamás, G., Thomson, A., Wang, Y., Yuste, R., & Ascoli, G. A.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- Children's Hospital of Philadelphia (US)
- Washington University in St. Louis (US)
- Centre National de la Recherche Scientifique (FR)
- Instituto de Neurociencias (ES)
- Forschungszentrum Jülich (DE)
- NYU Langone Health (US)
- Max Planck Florida Institute for Neuroscience (US)
- HUN-REN Institute of Experimental Medicine (HU)
- Stanford Medicine (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Icahn School of Medicine at Mount Sinai (US)
- National Institute for Physiological Sciences (JP)
- University of Debrecen (HU)
- University of Pittsburgh (US)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (US)
- Technical University of Munich (DE)
- German Cancer Research Center (DE)
- Brandeis University (US)
- Boston University (US)
- Inserm (FR)
- University of California, San Francisco (US)
- New York University (US)
- Howard Hughes Medical Institute (US)
- Yale University (US)
- George Washington University (US)
- University of Göttingen (DE)
- University of Szeged (HU)
- UCL School of Pharmacy (GB)
- Wenzhou Medical University (CN)
- George Mason University (US)
- Corresponding author
- DeFelipe, Javier
- Larrañaga, Pedro
- Ascoli, Giorgio A.
- ORCID
- DeFelipe, Javier
- Bielza, Concha
- Larrañaga, Pedro
- Anderson, Stewart
- Burkhalter, Andreas
- Cauli, Bruno
- Feldmeyer, Dirk
- Fishell, Gord
- Freund, Tamás F.
- Hill, Sean
- Hof, Patrick R.
- Jones, Edward G.
- Kawaguchi, Yasuo
- Kisvárday, Zoltán
- Kubota, Yoshiyuki
- Lewis, David A.
- Markram, Henry
- McBain, Chris J.
- Meyer, Hanno S.
- Monyer, Hannah
- Nelson, Sacha B.
- Rockland, Kathleen
- Rossier, Jean
- Rubenstein, John L. R.
- Rudy, Bernardo
- Scanziani, Massimo
- Shepherd, Gordon M.
- Sherwood, Chet C.
- Staiger, Jochen F.
- Tamás, Gábor
- Yuste, Rafael
- Ascoli, Giorgio A.
- Venue
- Nature Reviews Neuroscience
- Published
- 2013-02-06
- Volume
- 14
- Issue
- 3
- Pages
- 202-216
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC3619199
- OpenAlex
- W1995031373
- Cited by
- 833
- References
- 40
- Citations by year
- 2026: 23 · 2025: 44 · 2024: 48 · 2023: 57 · 2022: 62 · 2021: 56 · 2020: 72 · 2019: 71 · 2018: 63 · 2017: 72 · 2016: 67 · 2015: 86 · 2014: 79 · 2013: 33
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- JuSER (Forschungszentrum Jülich) (publishedVersion)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- HAL (submittedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- University of Debrecen Electronic Archive (University of Debrecen) (submittedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion)
- Archivo Digital UPM (Universidad Politécnica de Madrid) (submittedVersion, cc-by-nc-nd)
- GRO (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- École Polytechnique Fédérale de Lausanne
- Ministerio de Economía y Competitividad (C04-04, TIN2010-20900-C04-04)
- National Institutes of Health (5r01ns030989-22)
- U.S. Public Health Service (R01-39600)
- Japan Society for the Promotion of Science (21240030)
- National Institute of Mental Health (R01MH043784)
- National Eye Institute (R01EY012114, R01EY011787)
- National Institute on Deafness and Other Communication Disorders (R01DC009977, P01DC004732)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063720)
- National Institute of Neurological Disorders and Stroke (R01-NS030989, R01NS039600, R01NS075007)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- GABAergic interneurons, cortical interneurons, neuron classification, cerebral cortex, neuronal morphology, Bayesian analysis, clustering algorithms, supervised classification, community consensus, taxonomy
- MeSH terms
- Algorithms, Animals, Bayes Theorem, Cerebral Cortex, gamma-Aminobutyric Acid, Humans, Interneurons, Terminology as Topic, Cluster Analysis
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review