Dimos Karamanlis @dimokaramanlis@qoto.org

So cool: bringing the astronomical observatory concept to #neuroscience Awesome global #openscience that will hopefully help bring expensive techniques to less well-off labs!
#sfn #sfn23

1/ What is the organization of mouse visual cortex across regions?

In our latest work led by Rudi Tong and Stuart Trenholm, now out on bioRxiv (https://biorxiv.org/content/10.1101/2023.11.03.565500v1) we mapped the "feature landscape" of mouse visual cortex.

Here is a thread about what we found.

#neuroscience #neuralnetworks

Jonny Lovelace, Jingrui Ma, ... and Vinny Augustine discovered the vagal pathway underlying fainting, really exciting work, summarized here: https://www.nature.com/articles/d41586-023-03450-3 , full paper here: https://www.nature.com/articles/s41586-023-06680-7 (I helped a little w/ neural analyses)

When the mouse faints, its eyes roll back and most neurons across the brain *shut off completely* (at yellow line in first figure, shows one example #neuropixels recording). But neurons in the hypothalamic PVZ increased their firing during this time period (first group in the second figure). These neurons were causally implicated: inhibition increased fainting duration while excitation increased arousal.

#neuroscience #syncope #fainting #vagal #discovery

New preprint! "Tracking neurons across days with high-density probes", by Enny Van Beest, Celian Bimbard and team.

Chronic #Neuropixels probes can record from the same neurons for days, but require new approaches for tracking neurons.

Enny and Celian developed UnitMatch, which operates after spike sorting and relies only on the neurons' average spike waveform.

They then validated the results with functional responses – which were remarkably stable!

https://www.biorxiv.org/content/10.1101/2023.10.12.562040v1 (1/2)

Cosyne abstract submissions are OPEN and will close Nov 19th!

This is the 21st Cosyne 😱 and the 20th anniversary 🤓 so make sure to submit and join us in Lisbon!!

🥳🧠 🥳🧠🥳🧠🥳🧠🥳
https://www.cosyne.org/abstracts-submission

I'm happy to announce the start of a new free and open online course on neuroscience for people with a machine learning or similar background, co-developed by @marcusghosh. YouTube videos and Jupyter-based exercises will be released weekly. There is a Discord for discussions.

For more details about the structure of the course, and to watch the first video "Why neuroscience?" go straight to the course website:

https://neuro4ml.github.io

Currently available are videos for "week 0" and exercises for "week 1", but more coming soon.

Why did I create this course? Well, I think both neuroscience and ML can be enriched by knowing about each other and my feeling is that a general purpose intro to neuro or comp-neuro isn't the right way to inspire people in ML to be interested in neuro.

I hear a lot about neuroscience inspiring AI, but I think there's understandable scepticism about that from ML people. I don't want people to take neuro ideas and apply directly to ML, I just think we get a richer picture of what both fields are doing if we think more widely.

In other words, we should be thinking that we are somehow studying the same problem in different ways. You see that in the early history of the field, and it's very inspiring. (Yes, this is pretty much just saying that cognitive science is cool, but my scope is a bit narrower.)

The focus then is not on how neuroscientists think the brain works, but on the mechanisms the brain uses. These are strange, inspiring, and often their contribution to intelligent behaviour is still deeply mysterious.

The first video of the main part, on the structure of neurons, finishes with recent research (from @ilennaj and @kordinglab among others) on what the function of dendritic structure might be. No answers, just ideas.

And that's going to be another key part of this course. Research level problems are not hard to find in neuroscience, and the aim of this course is to empower students with the tools to start finding and working on them straight away.

Most of the exercises in the course won't have correct answers. They're starting points for further investigation. We'll be downloading and exploring open neuroscience datasets using methods from computational neuroscience and ML.

The course is not supposed to be comprehensive. It's a short course and the aim is more to get inspired and start on a longer road. I'd expect everyone to get something different out of it, and I'm happy if for some people their take home is "neuroscience is not for me"!

In some ways, it's the course I would have liked to get me into neuroscience and for my incoming PhD students from non-neuro backgrounds to be able to take. It's personal, and full of the sort of stuff that inspires me to be interested in neuroscience.

Well, I hope that some of you might be interested to follow along in the next few weeks, and since it's the first time I'm giving this course please do give feedback by email, Discord or however you like. Also, please feel free to re-use materials however you like.

#neuroscience #compneuro #machinelearning #ML

Our Perspective on reconstructing computational system dynamics from neural data finally out in Nature Rev Neurosci!
https://www.nature.com/articles/s41583-023-00740-7

We survey generative models that can be trained on time series data to mimic the behavior of the underlying neural substrate.

leaving aside the specific data issues (improbable duplications) in this Sûdhof paper, the data have been shared and it may be an eye-opener for some people to see how the sausage is made in an electrophysiology paper. Yet another reason to require data sharing as standard policy.

https://pubpeer.com/publications/DAF32F6DB6C166337E5381F769AE52

A primer on deep-brain optical recording of neural dynamics during behavior. https://authors.elsevier.com/c/1htVZ3BtfH5Mxi

Detailed considerations and tradeoffs regarding deep-brain fluorescence recording techniques-- a comprehensive guide for all major steps involved, from project planning to data analysis.

On a different note, a cool paper: https://www.science.org/doi/10.1126/sciimmunol.adf7702

The hygiene hypothesis has been a major idea floating for a long time. New study shows mice exposed to a more wild microbiome still develop allergies just the same.

What does having a diverse #Microbiome 🦠 actually do? Maybe a reason to focus more on what microbes are present (even in low abundance) and less on indices. #Immunology

A warm welcome to @biorxivpreprint who now have several accounts on biologists.social. You can follow them per subject category.

Find out more in their latest news post: https://connect.biorxiv.org/news/2023/09/27/biorxiv_expands_on_social_platforms

The reason p-values are still used is that the only coherent criticism on p-values is 'That is not the question you should be asking' and most scientists simply disagree with that proposition.

How does experience affect representational drift in the visual system? We provide new insights from the brilliant Joel Bauer, Uwe Lewin and @_eherbert as they study V1 through extensive longitudinal recording & modeling—highlighting the impact of experienced oriented contours!:

Tweeprint here:

https://x.com/Neuro_Joel/status/1706203566162038922?s=20

Very happy to be part of a productive team effort of
Joel Bauer, Uwe Lewin, Julijana Gjorgjieva Carl Schoonover, Andrew Fink, Tobias Bonhoeffer and Mark Hübener!

Sharing new preprint from our lab!
@biorxivpreprint

Led by Paul LaFosse, we show neurons in the awake 🧠can filter out inputs: attenuation-by-suppression.

Also: real neurons’ activation function share features w/ #ai systems (eg ChatGPT).

Comments welcome!

Thread. #neuroscience #ai #NeuroAI #gpt #chatgpt 1/15
Preprint- https://www.biorxiv.org/content/10.1101/2023.09.13.557650v1

Wow! 124 brain researchers call out what journalists call the "leading theory of consciousness" (integrated information theory, IIT) as pseudoscience.

https://psyarxiv.com/zsr78/

💯​: We need testable theories about the brain to move forward. Every theory starts as a proto-theory (and that's fine). But when theories are not even wrong
https://en.wikipedia.org/wiki/Not_even_wrong
we must acknowledge that.

Especially when the stakes are as high as they are here, with big ethical implications (eg for organoids and coma patients, as the authors describe).

Introducing Neuropixels Ultra, a new probe with >10x site density: an implantable voltage camera capturing complete planar images of neurons' electrical fields in vivo! ⬆️ spike sorting yield, ⬆️ detection of small fields, and ⬆️ cell type identification.🧵
https://www.biorxiv.org/content/10.1101/2023.08.23.554527

Excited to share new work from the lab.

Temporal regularities shape perceptual decisions and striatal dopamine signals.

https://www.biorxiv.org/content/10.1101/2023.08.18.553829v1

Huge effort from Matthias Fritsche and co-authors Antara Majumdar, Lauren Strickland, Samuel Liebana Garcia and Rafal Bogacz. And big thanks to our funders @wellcometrust @hfsp