Alex Weber @weberam2@qoto.org

I love how the White House definition of open science includes "processes" next to "product"

"The [OSTP] defines Open Science as the principle and practice of making research products and processes available to all..."

https://www.whitehouse.gov/ostp/news-updates/2023/01/11/fact-sheet-biden-harris-administration-announces-new-actions-to-advance-open-and-equitable-research/

Open Science is often overly focused on the end result. I think it will be much more impactful if we make the *process that creates knowledge* open and accessible to all from beginning to end.

The blog of the UK Society for the Study of Labour History has published this piece by me on building a list of a radical online archives and the importance of digitised open access left-wing historical documents for researchers and activists. https://sslh.org.uk/2023/09/19/dont-mourn-digitise-building-a-list-of-radical-online-archives/

“The open access movement is fighting to get rid of paywalls altogether, and we are making progress. But in the meantime, thanks to Alexandra’s courage and creativity, researchers around the world have SciHub. It is my honor and privilege to recognize her with a 2023 EFF Award.”

How does it all work together?

Are there proposals of how multiple functional-connectional systems work together? Not just individual systems, but jointly.

Eg of individual systems: basal ganglia loops, cortico-thalamic loops, amygdala bidirectional connections with cortex etc.

#neuroscience
@cogneurophys

Synaptic transmission is noisy, presumably because the brain tries to keep energy costs down.

When we modelled this energy cost in an ANN, it spent its energy cleverly: it made synapses that were important for the task more precise, and synapses that were less task-important more noisy.

Kind of shockingly, this is also what the brain should do if it were being Bayesian.

Top work by PhD student James Malkin, with @conorjh and @laurence_ai

https://arxiv.org/abs/2309.03194

@PessoaBrain @manlius @DrYohanJohn A phase of matter is defined by the average behavior of individual particles. E.g. a magnetized phase is where the magnetic moment of most particles are pointing in the same direction, a solid phase is when the motion of individual particles is confined to a very narrow range, etc.

So if we observe the behavior of individual particles for sufficiently long, we can tell in which phase the system is.

A phase transition requires a control parameter — e.g. temperature. So, if we vary this, and observe the behavior of individual particles, we can tell when a phase transition happened.

Now, *predicting* a phase transition means, I suppose, knowing the precise value of the control parameter where it happens.

To perform this computation we need to know, in general, how the particles interact.

But to define what a phase is, and to measure it, we do not.

This is a great little game for learning git! Love it 😃 https://ohmygit.org

Provocative

As a young psychologist, this chills me to my bones. Apparently is possible to reach the stratosphere of scientific achievement, to publish over and over again in “high impact” journals, to rack up tens of thousands of citations, and for none of it to matter. Every marker of success, the things that are supposed to tell you that you're on the right track, that you're making a real contribution to science—they might mean nothing.

(I agree: time to rethink that any one of us is how this works).

I’m so sorry for psychology’s loss, whatever it is

https://www.experimental-history.com/p/im-so-sorry-for-psychologys-loss

A good friend works in a department run almost exclusively by social psychologists. He was told to expect to publish (not submit... publish) 4 papers a year, and make sure at least a subset of them appear in high impact journals like PNAS, Science, Psych Science, etc. if he wanted to get tenure.

The field cannot then act shocked when many of its superstars are found to have committed fraud or (best case scenario) sloppy research practices. The system practically begs for it to occur in order to succeed.

https://www.theatlantic.com/science/archive/2023/08/gino-ariely-data-fraud-allegations/674891/

This is one of the most incredible pieces of research into a random piece of infrastructure I’ve ever seen. A wild ride.

https://tylervigen.com/the-mystery-of-the-bloomfield-bridge

h/t @danyork

@manisha
@FroehlichMarcel @lina @kordinglab @elduvelle @NicoleCRust @PessoaBrain
The work on DIY algos is pretty much done on the server side because its purposefully simple, I just havent had time to test and deploy it. Thats described here:
https://wiki.jon-e.net/Masto_Forks/DIY_Algorithms

The client side needs a bit more work, but I have models set up for storing the local info you need to compute them, then it'll need a facility for computing derived features and then a syntax for specifying how they are combined to yield a ranking. Thats here, similarly stalled because I have needed to do the thing I get paid for
https://git.jon-e.net/jonny/diyalgo/

Doing personalized algos in this way avoids the kind of mass surveillance that bsky is built on (you work off a local copy of the stuff you and your instance can see) and is arguably more scalable - nearly all bluesky feeds are global, ie. Everyone that follows them sees the same thing, because computing personalized feeds is pretty damn expensive for a free service. So I expect any interesting third party personalized feeds will rely on advertising or some other nasty scheme to pay the bills.

Now one of the problems of the #neuroscience community is that we're all split among multiple platforms. Really too bad.
And I really mean it. Aside from graduate school, from 2019 to 2022 was probably the time that I learned the most about the brain because of the vast amount of material shared on social media. It was great fun for some time. 😔

At some point on social media I saw a post about developing a publishing agreement with trainees who were leaving the lab. I thought it was a great idea, but now can't find where I saw it.

Does anyone have a resource like that they would be willing to share or have something they could point me to?

#academia #publishing

#ComplexityThoughts is back after the summer break!

In the issue #14 amazing new papers, from #NetworkScience foundations to #OriginOfLife #Neuroscience and #ComputationalSocialScience

Unraveling complexity: building knowledge, one paper at a time!
Not yet subscribed? It's never too late, and it's 100% free.

https://manlius.substack.com/p/complexity-thoughts-issue-14

I'm currently developing a new course "Neuroscience for machine learners" that I hope to be able to make publicly available, and I'd love to hear what you think should be in it.

It's aimed at people with a machine learning background to learn a bit about neuroscience. My thinking is that neuroscience and ML have had fruitful links in the past, and may again in the future (although right now they're drifting apart). This course is designed to give students the background they'd need to be able to discover, understand and make use of new opportunities arising from neuroscience (if they do). I'm not trying to tell them only about the bits of neuroscience that we already think are applicable to ML, but to give them enough background to read and understand enough neuroscience to allow them to make new discoveries about what might be applicable to ML. The constraint is that it can't just be an intro to neuro course I think, because I'm not sure how compelling that would be to students with an ML focus. The course is 10 weeks and will have quite a practical focus, with most of the attention on weekly coding based exploratory group work rather than lectures. (Similar to @neuromatch Academy.)

I have thoughts about what should be on this course, but I'd love to know what you all think would be most relevant.

#neuroscience #compneuro #machinelearning #ai