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This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through the Cajal Blue Brain (C080020-09; the Spanish partner of the EPFL's Blue Brain initiative) and TIN2016-79684-P projects, by the Regional Government of Madrid through the S2013/ICE-2845-CASI-CAM-CM project, by the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 785907 (HBP SGA2), by the German Federal Ministry of Education and Research grant 01GQ1406, and by the German Research Foundation grant CU217/2-1. LA-S acknowledges support from the Spanish MINECO scholarship at the Residencia de Estudiantes and from the UPM grant for the stay in the Ernst Strungmann Institute (ESI) for Neuroscience. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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A regularity index for dendrites - local statistics of a neuron's input space

Publicat a:Plos Computational Biology. 14 (11): e1006593- - 2018-11-01 14(11), DOI: 10.1371/journal.pcbi.1006593

Autors: Anton-Sanchez, L; Effenberger, F; Bielza, C; Larrañaga, P; Cuntz, H

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Neurons collect their inputs from other neurons by sending out arborized dendritic structures. However, the relationship between the shape of dendrites and the precise organization of synaptic inputs in the neural tissue remains unclear. Inputs could be distributed in tight clusters, entirely randomly or else in a regular grid-like manner. Here, we analyze dendritic branching structures using a regularity index R, based on average nearest neighbor distances between branch and termination points, characterizing their spatial distribution. We find that the distributions of these points depend strongly on cell types, indicating possible fundamental differences in synaptic input organization. Moreover, R is independent of cell size and we find that it is only weakly correlated with other branching statistics, suggesting that it might reflect features of dendritic morphology that are not captured by commonly studied branching statistics. We then use morphological models based on optimal wiring principles to study the relation between input distributions and dendritic branching structures. Using our models, we find that branch point distributions correlate more closely with the input distributions while termination points in dendrites are generally spread out more randomly with a close to uniform distribution. We validate these model predictions with connectome data. Finally, we find that in spatial input distributions with increasing regularity, characteristic scaling relationships between branching features are altered significantly. In summary, we conclude that local statistics of input distributions and dendrite morphology depend on each other leading to potentially cell type specific branching features.

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