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Beyond the frontiers of neuronal types: fuzzy classification of interneurons

  • 1, 2, 3Email author,
  • 1, 2,
  • 4, 5,
  • 6 and
  • 4
BMC Neuroscience201314 (Suppl 1) :P56

https://doi.org/10.1186/1471-2202-14-S1-P56

  • Published:

Keywords

  • Fuzzy Cluster
  • Edge Cell
  • Inhibitory Interneuron
  • Neuronal Type
  • Structure Continuum

Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells[1]. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets[2], identifies a small optimal number of model archetypes[3]. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes.

A ready-to-use software package allowing classification of neuronal data with standard tools (MATLAB, Python, ...) via this fuzzy clustering approach without the need for a reimplementation of the algorithmic aspects is in preparation.

Declarations

Acknowledgements

We acknowledge financial support by the German Federal Ministry of Education and Research (BMBF) via the Bernstein Center for Computational Neuroscience Göttingen (01GQ1005B), by the Human Frontier Science Program (RGY0070/2007) and by the Agence Nationale pour la Recherche (ANR 2011 MALZ 003 01). Anastassios Karagiannis was supported by a Fondation pour la Recherche Médicale fellowship (FDT20100920106).

Authors’ Affiliations

(1)
Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), Göttingen, Germany
(2)
Bernstein Center for Computational Neuroscience, Göttingen, Germany
(3)
Department of Mathematics, Technische Universität München, Garching, Germany
(4)
CNRS UMR 7102, Laboratoire de Neurobiologie des processus adaptatifs, Université Pierre et Marie Curie, Paris, France
(5)
Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London, London, UK
(6)
CNRS UMR 7637, Laboratoire de Neurobiologie et Diversité Cellulaire, ESPCI ParisTech, Paris, France

References

  1. Karagiannis A, Gallopin T, Dávid C, Battaglia D, Geoffroy H, Rossier J, Hillman EM, Staiger JF, Cauli B: Classification of NPY-expressing neocortical interneurons. J Neurosci. 2009, 29 (11): 3642-3659. 10.1523/JNEUROSCI.0058-09.2009. DOI: 10.1523/jneurosci.0058-09.2009PubMed CentralView ArticlePubMedGoogle Scholar
  2. Zadeh LA: Fuzzy sets. Inform Control. 1965, 8: 338-353. 10.1016/S0019-9958(65)90241-X.View ArticleGoogle Scholar
  3. Battaglia D, Karagiannis A, Gallopin T, Gutch HW, Cauli B: Beyond the frontiers of neuronal types. Front Neural Circuits. 2013, 7: 13-DOI: 10.3389/fncir.2013.00013PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Gutch et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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