🧠 New preprint by Kim et al. (2025) from David Anderson’s lab: A line #attractor maintains aggressiveness during feeding in “hangry” mice 🍔🐁. Using in vivo #CalciumImaging and #rSLDS modeling, they show how moderate fasting stabilizes an aggression-related attractor in #VMHvl, while prolonged fasting collapses it, linking hunger, motivation, and aggression through #PopulationDynamics:

🌍 https://doi.org/10.1101/2025.10.16.682711

#Neuroscience #CompNeuro #Behavior #AttractorDynamics #Hypothalamus #2p #imaging

For computational neuroscience / statistical physics people in Fedi: I am actively looking for a tenure-track or a junior PI position.

My background is pure statistical physics and complex systems. I mostly do stochastic processes but I'm very familiar with disordered systems too. I'm more interested by the dynamical systems side of things... How structure shapes dynamics, how dynamics are related to computation and processing . I am quite good at writing simulation code.

I also have taught a lot of hours and cosupervised students with good results. If you know about any open position or grant and you're interested in my profile, feel free to contact me!

#getfedihired #compneuro #computationalneuroscience #academicchatter #statphys

Is anarchist science possible?

As an experiment, we got together a large group of computational neuroscientists from around the world to work on a single project without top down direction. Read on to find out what happened.

The project started as a tutorial on a new technique at the @CosyneMeeting 2022. We realised that the technique was easy and cheap for anyone to use with a lot of low hanging fruit.

https://neural-reckoning.github.io/cosyne-tutorial-2022/

At the tutorial we announced a 1-2 year open research project that anyone could join, starting from the materials of the tutorial, and a few starting questions we found interesting, but with no other constraints. We were inspired by the Polymath Project in mathematics.

https://en.wikipedia.org/wiki/Polymath_Project

31 people contributed to the project, joining for monthly meetings to discuss progress. All code was publicly available throughout, and when we started writing up the work in progress was also fully public. You can see that version here: https://comob-project.github.io/snn-sound-localization/paper

Not everyone who was involved made it to the paper (didn't respond or couldn't find contact details), and not all are on Mastodon, but authors include: @marcusghosh @TomasFiers
@GabrielBena @rory

We used GitHub and Jupyter notebooks to coordinate development, with a website showing everyone's current code and results to make collaboration easier. We used @mystmarkdown and GitHub actions to automate this.

https://comob-project.github.io/snn-sound-localization/

So how did it work out? Well, some things went well and others not so well. We published a paper with our results and reflections on the process. If you're interested in spiking neural networks, sound localisation, or anarchist science, check it out:

https://www.eneuro.org/content/12/7/ENEURO.0383-24.2025

Generally, the infrastructure we built worked well, as did the monthly meetings. Starting from the tutorial was a good decision because it gave everyone a common reference and meant they could easily get started.

However, the lack of direction meant that we didn't achieve very coherent results in the end. We don't think this is a catastrophic problem, but when we try again, this is something we'd like to address. If you have thoughts or would like to be involved, get in touch!

Ultimately, we didn't achieve a scientific breakthrough in this project, but we did show that without top down direction or any specific funding, we could organise a large group of scientists to work together and publish their research in a good journal. We think that's a hopeful sign for the future!

#neuroscience #computationalneuroscience #compneuro #anarchism #science

Is anarchist science possible?

As an experiment, we got together a large group of computational neuroscientists from around the world to work on a single project without top down direction. Read on to find out what happened.

The project started as a tutorial on a new technique at the @CosyneMeeting 2022. We realised that the technique was easy and cheap for anyone to use with a lot of low hanging fruit.

https://neural-reckoning.github.io/cosyne-tutorial-2022/

At the tutorial we announced a 1-2 year open research project that anyone could join, starting from the materials of the tutorial, and a few starting questions we found interesting, but with no other constraints. We were inspired by the Polymath Project in mathematics.

https://en.wikipedia.org/wiki/Polymath_Project

31 people contributed to the project, joining for monthly meetings to discuss progress. All code was publicly available throughout, and when we started writing up the work in progress was also fully public. You can see that version here: https://comob-project.github.io/snn-sound-localization/paper

Not everyone who was involved made it to the paper (didn't respond or couldn't find contact details), and not all are on Mastodon, but authors include: @marcusghosh @TomasFiers
@GabrielBena @rory

We used GitHub and Jupyter notebooks to coordinate development, with a website showing everyone's current code and results to make collaboration easier. We used @mystmarkdown and GitHub actions to automate this.

https://comob-project.github.io/snn-sound-localization/

So how did it work out? Well, some things went well and others not so well. We published a paper with our results and reflections on the process. If you're interested in spiking neural networks, sound localisation, or anarchist science, check it out:

https://www.eneuro.org/content/12/7/ENEURO.0383-24.2025

Generally, the infrastructure we built worked well, as did the monthly meetings. Starting from the tutorial was a good decision because it gave everyone a common reference and meant they could easily get started.

However, the lack of direction meant that we didn't achieve very coherent results in the end. We don't think this is a catastrophic problem, but when we try again, this is something we'd like to address. If you have thoughts or would like to be involved, get in touch!

Ultimately, we didn't achieve a scientific breakthrough in this project, but we did show that without top down direction or any specific funding, we could organise a large group of scientists to work together and publish their research in a good journal. We think that's a hopeful sign for the future!

#neuroscience #computationalneuroscience #compneuro #anarchism #science

For everyone who can not attend the CCN Conference this year in amsterdam, all keynote lectures can be streamed here:

https://2025.ccneuro.org/keynote-lectures/

Full schedule with livestream links here:
https://2025.ccneuro.org/schedule-of-events/

First off, Nancy Kanwisher at 11.30 am (CET)

Edit: Not only keynotes but also symposia can be live streamed 🙂

#ccn2025 #neuroscience #cognitivescience #computationalneuroscience#CompNeuro

For everyone who can not attend the CCN Conference this year in amsterdam, all keynote lectures can be streamed here:

https://2025.ccneuro.org/keynote-lectures/

Full schedule with livestream links here:
https://2025.ccneuro.org/schedule-of-events/

First off, Nancy Kanwisher at 11.30 am (CET)

Edit: Not only keynotes but also symposia can be live streamed 🙂

#ccn2025 #neuroscience #cognitivescience #computationalneuroscience#CompNeuro

How can we test theories in neuroscience? Take a variable predicted to be important by the theory. It could fail to be observed because it's represented in some nonlinear, even distributed way. Or it could be observed but not be causal because the network is a reservoir. How can we deal with this?

Increasingly feel like this isn't a theoretical problem but a very practical one that comes up all the time. I'd be interested if anyone has seen anything practical that addresses this.

#neuroscience #compneuro