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Alexander Ecker

@aecker.bsky.social

438 followers 380 following 31 posts

Neuroscience, Machine Learning, Computer Vision • Scientist at @unigoettingen.bsky.social & MPI-DS • eckerlab.org • Co-founder of maddox.ai • Dad of three • All things outdoor

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Alexander Ecker @aecker.bsky.social · 10/08/2026

Had a great time with the team at our workshop at Schneefernerhaus on the Zugspitze last week! Great science and team discussions including three new people, alumni and special guests. Thanks everyone!

Sunrise on Zugspitze - Höllental and Jubiläumsgrat

Early morning hikers at the Zugsitze peak

Team photo at Schneefernerhaus at 2650 m above sea level

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Alexander Ecker @aecker.bsky.social · 11/04/2025

Using the power of the MICrONS dataset, we could show that our functional digital twin of the neurons could predict the basal bias of cells in lower layer 4 without this model having access to any morphological information. (10/12) www.nature.com/articles/s41...

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Alexander Ecker @aecker.bsky.social · 11/04/2025

We found a novel morphological trait in layer 4: neurons that are primarily located in V1 in a narrow stripe around the L4-L5 boundary. These neurons are atufted and avoid reaching into L5 with their basal dendrites. (9/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

There morphological differences between primary visual cortex (V1) and higher visual areas (HVA): In layer 4, atufted neurons are primarily located in V1, while tufted neurons are more abundant in areas AL and RL. (8/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

Neurons in layer 2/3 show strong trends with increasing cortical depth: (1) decreasing width of their dendritic arbor and (2) smaller tufts. (7/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

Dendritic morphologies vary with respect to three major axes: (1) the soma depth, (2) the total skeletal length of the apical dendrites and (3) the total skeletal length of the basal dendrites. (6/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

Dendritic morphologies form mostly a continuum, with distinct clusters only in deeper layers (e.g. layer 5 ET neurons). A quantitative test using synthethic surrogate data suggested that from layer 2 throughout upper layer 5 no density gaps exist and dendritic morphologies change continuously (5/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

Our learned embeddings capture the essence of the 3D morphology of neurons and reflect known excitatory cell types from mouse V1. (4/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

We employed GraphDINO, a self-supervised method for learning representations of neuronal morphologies without relying on manual annotations. The model outputs a vector embedding for each neuron that captures the morphological features of its dendritic tree. (3/12)

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Alexander Ecker @aecker.bsky.social · 11/04/2025

The data underlying our analysis is from the MICrONS consortium: a ~1mm³ volume of tissue from the mouse visual cortex, densely reconstructed using serial section electron microscopy, segmented into more than 54,000 individual neurons, among them ~30,000 excitatory ones. (2/12)

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