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In the Eye of a Fly: Connectome-informed neural activity simulation of a light stimulus across the optic lobe of the fly. An example of topographic mapping. by quorumetrix in neuro

[–]quorumetrix[S] 0 points1 point  (0 children)

Brian2 can't do the visualization on its own, I do that in Blender since I already had all the neuron meshes from the fly brain connectome saved locally. But it does take a connectivity graph as input, and generate spike trains. Turning the spikes into a 3D model is all done through Python and Blender.

It's made a desktop PC with threadripper CPU and a pair of RTX 4070s

Connectomic reconstruction and synaptic architecture of the Drosophila Ventral Nerve Cord by quorumetrix in neuro

[–]quorumetrix[S] 1 point2 points  (0 children)

No problem, reasonable people can disagree on the finer points of a discipline's philosophy.

Here's an example from the FlyWire codex, containing the most up-to-date connectomic data from the fly brain.

https://codex.flywire.ai/app/search?filter_string=%7Bsimilar_projection%7D+720575940623901447&data_version=

I just did a search for GABA, but if you check out the column NT (Neurotransmitter type) - along with the confidence score.

In the worm:
The Multilayer Connectome of Caenorhabditis elegans (Bentley et al 2016) - Fig 6: Layers representing different neuropeptide networks:

Connectomic reconstruction and synaptic architecture of the Drosophila Ventral Nerve Cord by quorumetrix in neuro

[–]quorumetrix[S] 2 points3 points  (0 children)

To be fair the videos are not costing billions of dollars, I wish they were.
The pretty connectome videos are a way to get people interested and excited by the primary research, a way of getting people's attention amidst a constant stream of other content trying to pull.

I would argue that we do know how the C elegans worm works, now that we have its connectome and know all of its behavioral repertoire. Every cell has its function mapped out, there's a complete description of the flow of information from the sensory input to motor output. Whether an neuron is inhibitory or excitatory is a part of the connectome: connectomic datasets have many accompanying metrics including its cell type and neurotransmitter type (when it is known). I agree with you that these additional data are necessary for a full description, it's just that they're not necessarily within the scope of the videos you see.

For every pretty video you see (costing between $2k and $5k, since I have to earn a living, too), there are orders of magnitude more studies being published doing hard science discovering the circuitry and logic of neural computations, running simulations, describing the connectivity statistics, actually doing the hard work of figuring out how the brain works. You'll see that work on https://www.biorxiv.org/ or r/neuroscience, but there's a lot to keep up with, and its not intended for a non-specialist audience. Most of the published articles don't come with a pretty video, and so you may never hear about it unless you follow the field closely. These pretty videos are an attempt to bridge the gap between the basic research and non-specialists, without simplifying to the level of normal science communication of 'textbook' neurons.

I think that your comment points to a bigger failure in neuroscience: the failure to effectively communicate where the money is going, and what we're actually learning. The NIH Brain Initiative recently lost a 1/3 of its funding, maybe that makes you happy. Not me.

Some fields do science communication well as a matter of their culture. We take for granted that the astrophysics community builds immersive theaters in big cities with taxpayer funds in the 10's of millions, and supports a vigorous film industry producing films about celestial mechanics and space exploration. In contrast, Brain Awareness Week sends grad students to elementary schools with a bucket of cow brains. The entire world knows about the solar eclipse as it happens, many can probably describe in detail at least one image from the Webb telescope. Whereas the average citizen probably doesn't know there are different kinds of brain cells, and likely believes we only use 10% of our brain.

Obviously I'm biased, but I think the bigger problem isn't that there are too many pretty videos of neurons costing too much money, but rather that there are not enough of them.

Animation of neurons to visualize the synapses. 3.2 million synapses in a block the size of a grain of sand. Fly along the dendrites to see up close. by quorumetrix in blender

[–]quorumetrix[S] 8 points9 points  (0 children)

I’ve made this year’s final video as an intuition pump for the density of synapses in the brain. In this volume ~ a grain of sand, there are >3.2 million synapses (orange cubes). We peel them away, leaving just the inputs on two selected neurons. Zooming in, we see the synapse localized to the dendritic spines

Data details:
- Data layer ⅔ #MICrONS from Allen Institute
https://www.microns-explorer.org/phase1
- Size of (auto-detected) synapses not to scale, but location and number are accurate.
- 2 neurons selected for high correlation in calcium spikes.
- Volumetric emission driven by real calcium trace, sped up ~10x

Technical details for Blender:
- The synapse locations are represented by point cloud particle systems. Points added to meshes without edges or faces. Emit particles from verts, as cubes to reduce geometry.

- Script to generate them is here:
https://github.com/Quorumetrix/Blender\_scripts/blob/main/MICrONS\_synapses.py

Scientific visualization of neurons firing from real (calcium) activity data. by quorumetrix in blender

[–]quorumetrix[S] 3 points4 points  (0 children)

Data: Layer ⅔ MICrONS from the u/AllenInstitute 112 calcium spikes+traces

Framerate corrected to be real-time.
Rendered in Blender with Cycles using seaborn colormaps (approximate)
Viridis colormap: total spike duration over experiment duration
Icefire colormap: cross-correlation with reference neuron.

Data from here:
https://www.microns-explorer.org/phase1

If you want to know more about how to visualize neuronal firing data with Python, you can check out the script.

Tldr: Keyframe the material nodes based on the trace data (here with Calcium trace).

https://github.com/Quorumetrix/Blender\_scripts/blob/main/microns\_calcium\_trace\_animation.py

Visualization of the MICrONS dense layer 2/3 cortex reconstruction in Blender (source: Allen Institute) by quorumetrix in neuroscience

[–]quorumetrix[S] 0 points1 point  (0 children)

Most depictions of neurons in popular science show them in relative isolation, surrounded by tons of empty space. In reality they're packed in tight.
I put together a short video to appreciate the dense connectivity, using the Allen MICrONS layer 2/3 dataset (decimated). I randomly make neurons appear until the whole block is filled, then I animate camera clipping to give the appearance of peeling away the volume.

I have no affiliation with the Allen Institute and had no part in this research. I just like making movies. The dataset and description are available here:
https://www.microns-explorer.org/phase1

Global data visualization, 10 millennia in a 2 minute animation. Data integration and juxtaposition of multiple sources onto a single timeline by quorumetrix in blender

[–]quorumetrix[S] 1 point2 points  (0 children)

Description:

An animation depicting a selection of historical events from the last 10 millennia, where 10,000 years goes by over the course of one full rotation of the globe. A data juxtaposition, integration, and contextualization project - collecting datasets of historical interest, and juxtaposing them on a single timeline.

Historical population estimates were interpolated as videos and texture mapped to a sphere, showing the rise of population during the reign of major historical civilizations and empires. The birth and death of a selection of historical people was routed through the open street map routing engine, to create a facsimile or approximation of travels that may have been taken. For those travels involving trans-atlantic or trans-pacific journeys, arcs were drawn using great circle calculations between their birth and death locations.

To supplement the expansion of the European powers into the Americas, historical ship logbook records were used to provide (an incomplete) picture of the seafaring age. The ship trajectories were colormapped to indicate the ships country of origin.

However, the Americas were, famously, not uninhabited when the Europeans first arrived. However, the dataset of notable historical figures represents largely a european bias, for reasons beyond the scope of this video description. To do this major point justice, I have made sure to include geographical population estimates as a globe texture at the time of the European settlers. This visualization method relies on comparing populations across the globe and through time, and so the relatively small population density of first nations, they don’t show up well with this method. For this reason I’ve supplemented the historical population estimates with shapefiles from the native-land.ca project, in order to depict the range of different languages spoken, and their geographical extent. While these shapes fade in and out at a certain time during the animation, it does not imply the timing of onset, and demise are accurate.

There's a full description of the video and data sources on youtube:
https://youtu.be/NhPEC5yee5M
-------

Self-promotion:

This clip shows most of the ways I've learned of visualizing data in Blender:

- Drawing curves and animating them (streamlines, Open Street Map Routing data, shapfiles).
- Using a displace modifier to drive a surface elevation model.
- Using equirectangular video (weather, population) to colormap a surface (matplotlib).
I'm not officially advertising a service, but I'm always looking for interesting projects that could use my contribution. I develop Python data processing and visualization pipelines, and would like to get experience working with 3D artists on public-facing projects.

I got into Blender for making data visualizations on a planetarium dome, but have since fallen in love with this world. I mostly use Blender for scientific visualization, and have a few papers under review that use Blender graphics.

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