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Casey Schneider-Mizell

@csdashm.com
642 followers 630 following 154 posts

Assistant Investigator at Allen Institute for Brain Science. Formerly Janelia, Universität Zürich, and U Mich Physics. Building bottom-up insight into the brain from synaptic resolution connectomics and making computational tools to help you do that too.

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Casey Schneider-Mizell @csdashm.com · 20/02/2026
From @bdpedigo.bsky.social: a super efficient, highly reliable, and very generalizable method for mesh structure classification, applied here to spine detection across basically every synapse onto a cell in the MICRoNS dataset.
Mesh analysis pipeline showing feature extraction and spine classification.
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Casey Schneider-Mizell @csdashm.com · 26/10/2025
You offer a confusing reading of the policy. Nowhere does it say that primary determinants should be anything other than relevancy and truth, and it very explicitly suggests that authors simply think a bit harder about distributing citation credit to avoid unthinking rich-get-richer-ism.
The aim of asking authors to consider citation diversity is not to require a specific level of representation for different groups in the reference list. Rather, we hope that our request serves as a nudge for authors to slow down and take the time to survey the field prior to writing, rather than relying on the same articles (and by extension, authors) that they have historically cited and therefore ‘come to mind’ first. Of course, some forms of diversity are more easily assessed than others — identifying an author’s institution and country of employment (markers of geographical representation) are more straightforward than identifying an author’s gender or ethnic identity. However, we hope that encouraging authors to think about citation diversity will prompt them to engage in concerted and sustained efforts to educate themselves about the relevant work of underrepresented scholars.
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
Also, it turns out that if you want to "down", you might also need to curve over. The descending projections of upper layer cells and deep layer cells in V1 coherently curve below layer 5. This matters a lot for measuring distance-dependent connectivity.
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
There were a few other interesting details that I couldn't emphasize but I really like. For example, it turns out there are cells that specifically target the apical dendrites (Iight pink) of deep layer neurons:
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
Some excitatory subtypes like layer 5 ET cells had particularly strong specificity, with their inhibitory neurons like this basket cell doing virtually nothing but connecting to ET neurons.
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
The third surprise was that this specificity was pervasive. Individual inhibitory neurons (columns here) not only put most of their synapses onto one or two excitatory clusters, but diverse "motif groups" of different inhibitory neurons had similar output budgets across target clusters.
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
But did this matter for the circuit at all? It turns out it did! For example, the inhibitory neurons that target layer 2 cells tend to talk to other layer 2 cells and not so much layer 3 and vice versa. Moreover, all layer 5 clustered seemed had quite different sources of inhibition as well.
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
But that's exactly what we found! And they look very different, with the SST-targeting population being largely bipolar cells, as expected, but the basket-targeting population being wispy bipolar cells. Now we have a new, highly specialized knob controlling cortical computations!
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
Reconstructing the axons of inhibitory neurons, we could reconstruct a columnar map of inhibition of inhibition. This revealed the first surprise! While it broadly agreed with the literature, in the upper right of this matrix, you see inhibitory neurons that only target basket cells (PeriTC here)
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
For the first time ever, we could look at many nearly complete neuronal arbors where we didn't just know their shape, but the size and location of all 4 million synaptic inputs. Individual cells had 1000-25000 inputs.
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Casey Schneider-Mizell @csdashm.com · 09/04/2025
The idea was to take a deep dive into the morphology and connectivity of cells sampled along a cortical column. There were ~1300 cells, 150 inhibitory and 1150 excitatory. (Render from @quorumetrix.bsky.social)
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Casey Schneider-Mizell @csdashm.com · 25/11/2024
In my work, I use morphology and synaptic connectivity in datasets like MICrONs to understand what kinds of cells exist in cortex, what rules govern how they connect, and what this network architecture might suggest about how the brain works. (Panel from www.biorxiv.org/content/10.1...)
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Casey Schneider-Mizell @csdashm.com · 25/11/2024
You can see its thick apical dendrite is totally covered in spines, each of which gets a synapse from an excitatory neuron.
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Casey Schneider-Mizell @csdashm.com · 25/11/2024
Here's a close-up of its cell body, which is pockmarked with inhibitory inputs from basket cells that inhibit it and help control its activity, perhaps because of the huge amount of input it gets.
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Casey Schneider-Mizell @csdashm.com · 25/11/2024
Look at this absolute unit of a neuron! This is a layer 5 ET neuron in mouse visual cortex, which gets a crazy amount of synaptic input (15,326 synapses in the dendrites here) and sends its outputs not just locally, but to subcortical areas that can more directly affect behavior.
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Casey Schneider-Mizell @csdashm.com · 11/11/2024
Neuroglancer, if you don't know, is a web app developed by Google (with a lot more community involvement recently) for exploring very large, cloud hosted 3d image volumes and segmentations.
A neuroglancer screenshot showing neuronal meshes and electron microscopy imagery.
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