An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex
Abstract
Abstract Neurons in the neocortex exhibit astonishing morphological diversity, which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological “bar code” describing more than 30,000 excitatory neurons in mouse visual areas V1, AL, and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2–3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5, which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons’ morphological diversity is better understood by considering axes of variation than using distinct m-types.
Article Details
Authors (48)
Marissa A. Weis
Stelios Papadopoulos
Laura Hansel
Timo Lüddecke
Brendan Celii
Paul G. Fahey
Eric Y. Wang
J. Alexander Bae
Agnes L. Bodor
Derrick Brittain
JoAnn Buchanan
Daniel J. Bumbarger
Manuel A. Castro
Forrest Collman
Nuno Maçarico da Costa
Sven Dorkenwald
Leila Elabbady
Akhilesh Halageri
Zhen Jia
Chris Jordan
Dan Kapner
Nico Kemnitz
Sam Kinn
Kisuk Lee
Kai Li
Ran Lu
Thomas Macrina
Gayathri Mahalingam
Eric Mitchell
Shanka Subhra Mondal
Shang Mu
Barak Nehoran
Sergiy Popovych
R. Clay Reid
Casey M. Schneider-Mizell
H. Sebastian Seung
William Silversmith
Marc Takeno
Russel Torres
Nicholas L. Turner
William Wong
Jingpeng Wu
Wenjing Yin
Szi-chieh Yu
Jacob Reimer
Philipp Berens
Andreas S. Tolias
Alexander S. Ecker