Beyond the frontiers of neuronal types
Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. However, a broad variability is generally observed even among cells that are grouped into a same class. In our Frontiers in Neural Circuits article in collaboration with Bruno Cauli (Paris), we propose a new unsupervised classification approach, based on fuzzy set theory, to account simultaneously for heterogeneity and existence of difference tendencies ruling variability. Curiously enough, our approach is sufficiently general for being applicable in virtually the same way to a complete different dataset including recordings of monkey vocalizations by Julia Fisher (Göttingen) & coworkers.
Specifically, 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. 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, identifies a small optimal number of model archetypes. 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.
To know more:
- D. Battaglia, A. Karagiannis, T. Gallopin, H.W. Gutch and B. Cauli, Beyond the frontiers of neuronal types, Frontiers in Neural Circuits 7:13 (2013).
And here the twin paper on monkey vocalizations 😉
- P. Wadewitz, K. Hammerschmidt, D. Battaglia, A. Witt, F. Wolf & J. Fischer. Characterizing Vocal Repertoires-Hard vs. Soft Classification Approaches. PLoS ONE 10, e0125785 (2015).