Dimos Karamanlis @dimokaramanlis@qoto.org

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

I don't have domain expertise to directly comment on the potential #LK99 room temperature superconductor, except to say that the experts that I have talked to are currently quite skeptical. But I can draw one analogy with my experience with mathematical research. A typical math research project consists of months of proposed attacks on a problem, resulting in all sorts of failures or partial successes, until enough experience and intuition is gained to locate the correct approach (or to realize that one needs to modify the problem, or work on a completely different project). However, when the time comes to write up the work, usually the failed or partial attempts are not mentioned at all, except perhaps as brief motivation for the final successful approach. This has some sense to it - a reader is likely to be more interested in the approach that worked than the approaches that didn't quite work - but can give the mistaken impression that good mathematics consists entirely of correct arguments, and that disclosing the failures one had to attempt before locating the correct approach is somehow shameful. But such failures are in fact enormously instructive, and I wish our culture was more open to sharing them.

With LK99, I have seen it reported that the initial announcements were released prematurely, while the research was still in the "partial success at best" stage. As such, the work fares poorly if judged by the usual standard of "successful, completed research", and criticism is due if one or more of the authors were presenting it as such. But as "research in progress, accidentally revealed to the public", I am inclined to be charitable, and wait for the sicence to play out.

Can you roll a ball with exactly enough energy to reach the top of a dome, and have it reach the top in a finite amount of time?

I'm going to idealize the hell out of this problem so we can easily study it using math. So: no friction, no air resistance... in fact, NONE of the sneaky stuff you're probably thinking about!

The problem is still tricky. For an ordinary dome the answer is *no*. If the ball has just enough energy to make it to the top, it rolls slower and slower as it gets near the top, in such a way that it never reaches the top.

But if the dome has a carefully chosen shape, the ball can reach the top in a finite time! This was pointed out by the philosopher John D. Norton, so it's called "Norton's dome".

For a full explanation go here:

https://sites.pitt.edu/~jdnorton/Goodies/Dome/

Thanks to @SylviaFysica for pointing this out!

Norton was mainly interested in another freaky feature of his dome. Say you start with a ball at rest on top of the dome. Then there are many solutions of Newton's law

F = ma

In one the ball remains at rest on top of the dome. But in others, it starts to roll down the dome in some arbitrary direction! Moreover it can start rolling at any time.

If you change the shape of the dome ever so slightly, this probably won't work. It needs to be crafted with perfect accuracy. So this is basically just a mathematical curiosity.

Math folks will realize what's going on: not every first-order differential equation has a unique solution given its initial value. But Norton, being a philosopher of physics, manages to make this a lot more exciting than a typical textbook treatment of the Picard–Lindelöf theorem. 🙃

Here's the math:

https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem#Example_of_non-uniqueness

Hi all! I'm a neuroscience postdoc studying information seeking and curiosity *in mice* in Richard Axel's lab at Columbia. I'll be on the academic job market this fall(!!!). Excited to be here.

My first post on Mastodon post -- any @Neurobio out there? @retina

the number of ppl perhaps rightly mourning the decay of academic twitter as one of the only ways to share, find and talk about research seems like it points directly to the fundamental hollowness of the current journal system as a means of uh sharing, finding, and talking about research.

our for-profit system of communication is so hostile to the way we want to work that we became dependent on a different, also very hostile informational chokepoint.

I think it is a pretty normal idea to want to try to rebuild our communication systems ourselves, in common. whatever that looks like. I get why that's weird or boring or scary to ppl I just think it would be fun and good to do.

The Electronic Frontier Foundation will award #AlexandraElbakyan, founder of the 'pirate' #library #SciHub, for her efforts to provide access to scientific knowledge. According to #EFF, #Elbakyan's site is a vital resource for millions of students and researchers. Some medical professionals have even argued that the site helped to save lives. https://torrentfreak.com/sci-hubs-alexandra-elbakyan-receives-eff-award-for-providing-access-to-scientific-knowledge-230728/

Make your next discovery using #Rastermap, a visualization method for large-scale neural data in #python. Paper now out: https://www.biorxiv.org/content/10.1101/2023.07.25.550571v1 (CLICK ON THE GIF) #neuroscience

(Hi all... pardon me for this mass-tagging...)

@WorldImagining yes!
@hugospiers:

For hashtags, also try out #Navigation #NeuroPreprint #NeuroPaper ... and some non-neuro (broken to not contaminate them): # Bloomscrolling # Mosstodon # Caturday # Raturday # SciArt

Here are some great accounts that I follow, and that should be active (in no specific order), you'll probably recognize most of them:

Neuro - oriented accounts:

@NicoleCRust
@PhiloNeuroScie
@achterbrain
@karihoffman
@katejjeffery
@paulgribble
@AllenNeuroLab
@Neurograce
@nadel
@markgbaxter
@albertcardona
@chrisXrodgers
@adredish
@dlevenstein
@vineettiruvadi
@GunnarBlohm
@jpeelle
@manisha
@DrYohanJohn
@alicia_izquierdo
@susanleemburg
@guidomeijer
@computingnature
@Andrewpapale
@MatteoCarandini
@UCL_NeuroAI
@ArminLak
@meganakpeters
@kordinglab
@neuralreckoning
@obarnstedt
@marcwhoward
@socneuronerd @kevinbolding
@olivia
@PessoaBrain
@BenoitGirard

Neuro-oriented groups (I broke the tag so they wouldn't boost this because it might be too much)

@ neuroscience@a.gup.pe
@ cogsci@a.gup.pe
@ cogneurophys@a.gup.pe
and the best one:
@ neurobuzz@a.gup.pe

Neuro-oriented instances that you can check the local feeds of:

https://fediscience.org/public/local
https://synapse.cafe/public/local
https://scicomm.xyz/public/local
(unfortunately our instance neuromatch.social does not have an open local timeline for now)

Less neuro-oriented accounts that are also great:
@jonny
@lisamelton
@alexwild
@inthehands
@liztai
@schoppik
@rodhilton
@Mrfunkedude
@emmatonkin
@rolle
@breadandcircuses
@tchambers
@theLastTheorist
@Em0nM4stodon (special mention to the great tips on her profile 👍​)
@futurebird
@ct_bergstrom
@alexwild
@artologica
@OkieSpaceQueen
@mariyadelano
@juliancday
Fediverse-related:

@FediFollows
@feditips

News:@arstechnica @Flipboard

I could add more but I would spend the night on it! I've only reached page 5 /27 of my 'follows and followers' tab (and this is a selection)

Hope this helps! Please don't hesitate to ask anything :)

🚨Pre-print alert 🚨Yes that’s right, we’re publishing not one, but two, pre-prints! Here we use the *electrophysiology data* from the Brain Wide Map as well as *widefield imaging data* to investigate the question “Where in the brain is prior knowledge represented?” Recordings in 267 brain regions from 121 mice (1/8)
#neuroscience
https://www.biorxiv.org/content/10.1101/2023.07.04.547684v1

Rats use #eyebrows to sense the wind! Animals use wind sensing (#anemotaxis) for navigation & survival. @annmclemens @BrechtLab &co use structural, ephys & behavioral analyses to reveal that supra-orbital #whiskers act as wind antennae in rats #PLOSBiology https://plos.io/3riXfQV

New study by Jacob L. Yates et al. introduces new tools enabling natural #behavioranalysis in untrained subjects, revealing accurate #visual #receptivefields and tuning curves in marmoset monkeys, highlighting the potential of free viewing for studying #neuralresponses and natural #behaviordynamics.

📔 Yates, Coop, Sarch, et al., "Detailed characterization of neural selectivity in free viewing primates", Nat Commun 14, 3656 (2023), https://doi.org/10.1038/s41467-023-38564-9

#Neuroscience

In an alternative history of the world, quantum mechanics could have been discovered by chemists following up on the theories of Clebsch and Gordan.

We now use their math to understand the funny way angular momentum 'adds' when we combine two quantum systems. We use this in chemistry.

But they were already suggesting to use this math for chemistry back in the 1890s. They were ignored by chemists! But it was very hard, back then, for people to believe that atoms were governed by the math of linear algebra and invariant theory - now called group representation theory.

According to Wikipedia:

Like James Joseph Sylvester, Paul Gordan believed that invariant theory could contribute to the understanding of chemical valence. In 1900 Gordan and his student G. Alexejeff contributed an article on an analogy between the coupling problem for angular momenta and their work on invariant theory to the Zeitschrift für Physikalische Chemie. In 2006 Wormer and Paldus summarized Study's role as follows:

"The analogy, lacking a physical basis at the time, was criticised heavily by the mathematician E. Study and ignored completely by the chemistry community of the 1890s. After the advent of quantum mechanics it became clear, however, that chemical valences arise from electron–spin couplings ... and that electron spin functions are, in fact, binary forms of the type studied by Gordan and Clebsch."

I learned this amazing piece of history from James Dolan. The Wikipedia quote is actually from the biography of the mathematician Eduard Study:

https://en.wikipedia.org/wiki/Eduard_Study#Valence_theory

On explanations in brain research:

A thread of the same idea comes up again and again in brain research. It's the notion that identifying the biological details (such as the brain areas/circuits or neurotransmitters) associated with some brain function (like seeing or fear or memory) is not a complete explanation of how the brain gives rise to that function (even if you can demonstrate the links are causal). To paraphrase:

Mountcastle: Where is not how https://www.hup.harvard.edu/catalog.php?isbn=9780674661882
Marr: How is not what or why http://mechanism.ucsd.edu/teaching/f18/David_Marr_Vision_A_Computational_Investigation_into_the_Human_Representation_and_Processing_of_Visual_Information.chapter1.pdf
@MatteoCarandini: Links from circuits to behavior are a "bridge too far" https://www.nature.com/articles/nn.3043
Krakauer et al: Describing that is not understanding how https://www.cell.com/neuron/pdf/S0896-6273(16)31040-6.pdf
Poppel: Understanding brain maps does not formulate "what about" the brain gives rise to "what about" behavior https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3498052/

Any other explicit references to add to this list? @Iris, @knutson_brain, Anyone?

Also, I imagine that some form of the opposite idea must also be percolating: the notion that 'algorithmic' descriptions of the type used to build AI will be insufficient to do things like treat brain dysfunction (where we arguably need to know more about the biology to, e.g., create drugs). Any explicit references of that idea? @albertcardona @schoppik, @cyrilpedia, Anyone?

Traditional computer software tools resemble the standard mathematical concept of a function 𝑓:𝑋→𝑌: given an input 𝑥 in the domain 𝑋, it reliably returns a single output 𝑓(𝑥) in the range 𝑌 that depends on 𝑥 in a determinstic fashion, but are undefined or give nonsense if fed an input outside of the domain. For instance, the LaTeX compiler in my editor will take my LaTeX code, and - provided that it is correctly formatted, all relevant packages and updates have been installed, etc. - return a perfect PDF version of that LaTeX every time, with no unpredictable variation. On the other hand, if one tries to compile some LaTeX with a misplaced parenthesis or other formatting problem, then the output can range from compilation errors to a horribly mangled PDF, but such results are visually obvious to detect (though not always to fix).

#AI tools, on the other hand, instead resemble a probability kernel μ:𝑋→Pr(𝑌) rather than a classical function: an input 𝑥 now gives a random output sampled from a probability distribution μₓ that is somewhat concentrated around the perfect result 𝑓(𝑥), but with some stochastic deviation and inaccuracy. In many cases the inaccuracy is subtle; the random output superficially resembles 𝑓(𝑥) until inspected more closely. On the other hand, such tools can handle noisy or badly formatted inputs 𝑥 much more gracefully than a traditional software tool.

Because of this, it seems to me that the way AI tools would be incorporated into one's workflow would be quite different from what one is accustomed to with traditional tools. An AI LaTeX to PDF compiler, for instance, would be useful, but not in a "click once and forget" fashion; it would have to be used more interactively.

I wrote a longer-form piece over at post.news about the problems with using the verb "hallucinate" to describe AI chatbots that make things up.

Here's the link for those that what it to read it formatted there.

https://post.news/article/2Lr1Pj6ITLA0LGxLi2CQIHUI1WB

I'll serialize it here as well, below.

Trained in an academic setting, I have often felt a little uneasy conveying science beyond my expertise.

@markdhumphries writes that this is not only okay, but actually highly encouraged for good popular science writing. He advocates doing research on the topic and conveying your own journey through learning.

https://medium.com/the-spike/its-a-good-thing-when-i-don-t-know-what-i-m-writing-about-6d0ce69d5629

#writing #science #PopularScience

#migration#re-Introduction.

I'm a theoretical neuroscientist at U Mainz Medical Center and co-affiliated with U Bonn Medical center. Primary focus on cortical circuits, their network activity, synaptic plasticity and protein dynamics in dendrites. Broadly interested how circuits learn e.g. neuro/AI interface and want to understand how neural networks compute, both algorithmically and intracellularly. Occational posts about societal and academic issues.

@jacklerner I would remind students: Whenever you read a productivity tip or lessons from someone's process, ask yourself: Who's taking care of the house and kids? Who manages their finances? Who's booking their flights and tracking their expenses and picking up their laundry? Do they have any disabilities, health issues, other challenges?

Do not beat yourself up if you're not able to be as productive as someone with significantly more privileges re: time. We don't all have the same 24 hours.

My little book is now officially published by Cambridge Univ Press and available for free here https://www.cambridge.org/core/elements/attending-to-moving-objects/0914ABF2EF7D03676124F7250874071A . I draw lessons from object tracking research to illuminate the nature of the bottlenecks on human visual processing.

The official version is available for free for two weeks. The version I published with bookdown (https://tracking.whatanimalssee.com/intro.html#summary) will be available for free at least until I die, or become too ashamed of the book. #attention #perception
#openaccess @cognition