Dimos Karamanlis @dimokaramanlis@qoto.org

I wrote about the cerebellum for Current Biology … check it out if you’d like to learn more about “the little brain” [link provides free access through February]
https://authors.elsevier.com/a/1iOZX3QW8S2T6A

Just getting a chance now to look at @strawman 's new system for following and keeping in focus flying insects with a telescope, including from a drone. The videos are amazing. There's surprisingly limited video of insects flying with the spatiotemporal resolution to track and analyze their wing and body movements.
Andrew's post on this work:
https://social.coop/@strawman@sciencemastodon.com/111624685212157804
Paper on bioRxiv: https://www.biorxiv.org/content/10.1101/2023.12.20.572558v1
Check out the videos in the supplement there, sneak peak gif:

Who did it better?

I'm a neuroscientist and group leader of the Flexibility in Circuits and Behaviour lab at SISSA, Italy. My lab studies how innate behaviours can be flexibly adjusted to contextual information, and how this correlates with changes in visual and behavioural encoding. We're a systems neuro lab with a neuroethological angle, and aim to identify general principles of how essential, very fast and stereotyped behaviours/circuits can be flexibly and quickly modulated. 🐁
www.reinhardlab.org

The mastodon community seems to have grown, so I'm giving it another try and hope for many stimulating inputs and discussions! 🧠😊

Conda is moving our social media presence from Twitter/X to Mastodon and LinkedIn at the start of 2024. It's past time to move into spaces that are welcoming and more in line with our community values. Going forward, you can find us at
🐘 @conda (https://fosstodon.org/@conda) on Mastodon
🔗 Conda Community (https://linkedin.com/company/condacommunity) on LinkedIn

Announcement: https://conda.org/blog/2023-12-27-social-move
We hope to see you on Mastodon and LinkedIn in 2024!

#introduction , hi , I'm a junior group leader at the Institute of Cellular and Integrative Neuroscience in Stasbourg. I am interested in multi-regional optical imaging (such as multi-fiber photometry and wide -field imaging). I use these approaches to study adaptation of multi-regional calcium dynamics and release of norepinephrine during learning and sleep.

#Retinas in awake mice respond more quickly and strongly than isolated tissue samples, highlighting the importance of studying them in live animals. #Neuroscience https://elifesciences.org/digests/78005/the-eye-of-the-beholder?utm_source=mastodon&utm_medium=social&utm_campaign=organic

A quick selection of interesting papers from this year:

1) A level-headed intro to neural manifolds and how they tie into our current scientific project
https://nature.com/articles/s41583-023-00693-x by Langdon et al.

2) A review on brain circuits of spatial navigation in fruit flies and other insects.
https://annualreviews.org/doi/abs/10.1146/annurev-neuro-110920-032645 by Wilson.

3) A philosophical treatment of constraint, which is a form of non-causal explanation that's kind of trendy right now.
https://link.springer.com/article/10.1007/s11229-023-04281-5 by Ross.

4) A sharp piece on the computational capacities of neural oscillations, and what they can and can't do for syntactical processing.
https://nature.com/articles/s41583-022-00659-5 by Kazanina & Tavano

(My 2c: https://sandervanbree.com/posts/1497-the-scope-and-limits-of-oscillations-in-language-comprehension)

5) A prudent analysis on how neural oscillations relate to representation, leveraging the always useful causal/constitutive distinction.
https://philpapers.org/rec/MARNOA-7 by Martínez & Artiga.

6) A model of space and concept learning with both algorithmic and neural commitments.
https://science.org/doi/full/10.1126/sciadv.ade6903 by Mok & Love

Neural activity across cortex is high-dimensional and ... complicated. Still, those fine neural features can be predicted from orofacial behaviors in mice! Great work by Atika Syeda et al in the lab!

Try out the all-new keypoint-based #facemap, paper here: https://www.nature.com/articles/s41593-023-01490-6

Our model for prediction mismatch responses in cortex is now on biorxiv.
We predict that the mismatch size influences the latency of the response, which would not be expected for error neurons in previous predictive coding models.
doi.org/10.1101/2023.11.16.567335

For the larger context, see your perspective paper
doi.org/10.1016/j.tins.2022.09.007

Congratulations to Kjartan van Driel to his first paper submitted!
And thanks to Lucas Rudelt and Fabian Mikulasch for designing this project!

@Neurograce @tyrell_turing @DrYohanJohn the comparison to psychology is good actually, and arguably we could learn from their experience. I do sometimes feel like our current approach is a bit like a scientist in the mid 20th century randomly picking 10 people, measuring their height and IQ, noting that they correlate fairly well and concluding that the study of height is a promising avenue for throwing light on intelligence.

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