Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification
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ORIGINAL ARTICLE
Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification Netanel Ofer1,2 · Orit Shefi1,2 · Gur Yaari1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions. Keywords Interneuron classification · Neuromorphology · Neuronal coding
Introduction Quantitative analysis of neuronal types and their properties is critical for better understanding and deciphering brain function (Harris and Shepherd 2015; Tremblay et al. 2016). Despite the attempts to standardize the terminology for neuronal types, there is no clear consensus regarding neuron nomenclature, leaving neuronal classification as an ongoing challenge (Arma˜nanzas and Ascoli 2015; Zeng and Sanes 2017). To date, interneuron classification is based on morphology (DeFelipe et al. 2013), membrane and firing patterns (Druckmann et al. 2012; Ferrante et al. 2016),
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12021-020-09466-8) contains supplementary material, which is available to authorized users. Orit Shefi
[email protected] Gur Yaari
[email protected] 1
Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
2
Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
connectivity patterns (Jiang et al. 2015), neurochemical markers (Kepecs and Fishell 2014), transcriptome (Zeisel et al. 2015; Tasic et al. 2016; Luo et al. 2017; Yuste et al. 2019; Gouwens et al. 2020), and epigenomics (Chev´ee et al. 2018). The morphology-based classification approaches include dendritic tree geometry (Helmstaedter et al. 2008; Papoutsi et al. 2017) and axonal projection (Mihaljevi´c et al. 2018; Gouwens et al. 2019), where directionalities of axons are taken into account. The interneuron’s axonal tree arbor enables better classification of cell types than the dendritic tree (Dumitriu et al. 2006; Jiang et al. 2015). Topological persistence-based methods were also developed to support comparisons between individual neurons and classification of neurons (Hern´andez-P´er
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