Dimos Karamanlis @dimokaramanlis@qoto.org

Boost this toot if you're planning on sticking around Mastodon & the Fediverse whether or not it's more popular than Bluesky.

New approach enhances neural implants for vision restoration.
https://elifesciences.org/articles/83424?utm_source=mastodon&utm_medium=social&utm_campaign=organic

my (personal) summary and then comments for this #JournalClub

any corrections, comments, additional questions are welcome, especially from the first author @jessetm

1/7
The main goal of this paper is to test if #VTEs (Vicarious Trial-and Error) and medial prefrontal cortex LFP relate to navigation behaviour parameters such as behavioural flexibility, performance and strategy use, during allocentric* navigation.

VTEs are a behaviour that rodents and humans do at choice points, looking alternatively at the different available options before choosing one (check video below). They have been studied mostly during response-based tasks (when the subjects have to learn a body-oriented response or sequence of responses to the reward). From that research, two possible roles for VTEs have been suggested: deliberation (weighing down the available options) or uncertainty (hesitation).

The current paper aims to test which is the most likely role of these two, by having a task involving a lot of deliberation and a lot of uncertainty (protocol explained below).
The main conclusion is that these two VTE types actually exist, which means VTEs should not just be interpreted as a marker of behavioural flexibility or deliberation. There is are also some interesting findings about different LFP rhythms in the medial prefrontal cortex being stronger during different types of behaviours (explained below).
(*) (allocentric = based on an external reference frame, like the Water maze task)

1/7
cc: @Andrewpapale, @drdrowland, feel free to add your comments /questions anywhere you want!

Huge congrats to @karyna-mi.bsky.social (shes not on here) for her paper published today in Science! She found that the hippocampus is really important for a key strategy we use to make decisions called hidden state inference! 🧪 🧠 1/7 https://www.science.org/doi/10.1126/science.adq5874 #neuroscience

We wrote a review on analysis methods for large-scale neural recordings https://www.science.org/stoken/author-tokens/ST-2239/full @marius10p #neuroscience 🧠

Anything we missed? Reply w/ your fav method!

What does #neuroscience tell us about AI, and vice versa?

In this new PNAS paper, we find that real neurons' activation functions (f-I curves) share features with freq-used AI activation functions.

We measure many neurons w/ 2p holographic stim.

Work led by Paul LaFosse... 1/3 🧠📈 🧪

New programme by Dutch Research Council NWO: academic journals that wish to 'flip' from a subscription model to diamond open access (no fees for readers - no fees for authors) can apply for funding to transition: https://www.nwo.nl/en/news/funding-for-flipping-journals-to-diamond-open-access
#OpenAccessWeek

2/ More recent patch recording from Large Bistratified GCs show a consistent ON-OFF response to the L+M stimulus, quite distinct from that of the small BS GCs. These results also suggest that the input from L and M cone circuitry to the LBGCs might be distinct (Kim, Packer & Dacey, 2024). How?
https://www.pnas.org/doi/10.1073/pnas.2405138121

📢 #Rastermap paper out now, easily explore and visualize your neural recordings 🐭🐒🐟 and ANNs 🤖 https://nature.com/articles/s41593-024-01783-4. Updates from preprint include analyses of primate data and IBL task data.

Video tutorial: https://youtu.be/oQHq7yUWn2k #Neuroscience #MachineLearning

Yay! John Hopfield and Geoffrey Hinton won the physics Nobel for their work on neural networks... work that ultimately led to modern-day machine learning.

Some of you are wondering why they got a *physics* Nobel.

In the 1980s, Hopfield invented the 'Hopfield network' based on how atomic spins interact in a chunk of solid matter. Each atom's spin makes it into a tiny magnet. Depending on the material, these spins may tend to line up, or point opposite to their neighbors, or interact in even more complicated ways. No matter what they do, at very low temperatures they tend to minimize energy.

A Hopfield network is a simulation of such a system that's been cleverly set up so that the spins store data, like images. When the Hopfield network is fed a distorted or incomplete image, it keeps updating the spins so that the energy decreases... and works its way toward the saved image that's most like the imperfect one it was fed with.

Later, Geoffrey Hinton and others generalized Hopfield's ideas and developed 'Boltzmann machines'. These exploit a discovery by the famous physicist Boltzmann!

Boltzmann realized that the probability that a chunk of matter in equilibrium has energy E is proportional to

exp(-E/kT)

where E is its energy, T is its temperature and k is a number now called Boltzmann's constant. When the temperature is low, this makes it overwhelmingly probable that the stuff will have low energy. But at higher temperatures this is less true. By exploiting this formula and cleverly adjusting the temperature, we can make neural networks do very useful things.

There's been a vast amount of work since then, refining these ideas. But it started with physics.

https://arstechnica.com/ai/2024/10/in-stunning-nobel-win-ai-researchers-hopfield-and-hinton-take-2024-physics-prize/

We’re excited to welcome @markdhumphries author of “The Spike,” as our new columnist. Check out his first piece, in which he explores how averaging is a convenient fiction of neuroscience.

https://www.thetransmitter.org/neural-coding/averaging-is-a-convenient-fiction-of-neuroscience/?utm_source=mastodon&utm_medium=org-social&utm_campaign=20240923-averaging-convenient

Researchers at SWC have revealed how sensory input is transformed into motor action across multiple brain regions in mice.

“We found that when mice don’t know what the visual stimulus means, they only represent the information in the visual system in the brain and a few midbrain regions. After they have learned the task, cells integrate the evidence all over the brain,” Dr Michael Lohse

Read the story: https://www.sainsburywellcome.org/web/research-news/brain-wide-decision-making-dynamics-discovered

Check out the full paper in Nature: https://www.nature.com/articles/s41586-024-07908-w

Live mouse (skin) #tissueclearing: Turns out, yellow food dye Tartrazine reduces scattering in the red/NIR range and allows temporary transparency in live tissues (e.g. mouse skin)
Achieving optical transparency in live animals with absorbing molecules
Ou et al., Science 2024
https://doi.org/10.1126/science.adm6869
Write-up: https://doi.org/10.1126/science.adr7935

#invivo #imaging #microscopy

A new mystery from the land of automatic publication.

https://pubpeer.com/publications/27EFCC358271321ED3680598F2BFCF#2

Finally had a chance to listen to this Mindscape podcast w/ Doris Tsao. SO GREAT! Whether you’ve been studying vision forever or know zero about it (a rare place to hit).

https://www.preposterousuniverse.com/podcast/2024/07/29/284-doris-tsao-on-how-the-brain-turns-vision-into-the-world/

“That's what's great about being a vision scientist: you just open your eyes and right there is the miracle you're trying to explain.” - Doris Tsao

Really exciting to hear them talk about the big C (onsciousness) - the reason so many of us opted in (and for various reasons abandoned). To see Doris take it on explicitly: yes!!! 👏👏👏. I can’t wait to see what she & her team make of it.

After all, this book was written 30 years ago. It was all about leveraging vision to study what consciousness is all about. Things have happened but haven’t quite gelled. Maybe - just may e - we’re ready …???

https://en.m.wikipedia.org/wiki/The_Astonishing_Hypothesis

@alexh

Too many people review previous work as if they are complete instead of data points. For that matter, too many people think of their own scientific results as if they are the only experiment evah and complete answers to questions.

In my lab, we never "show", nor do we ever say someone else "showed". We say we "found", and that others "found"... they found that in that experiment, under these conditions, on that day, something happened.

The "replication crisis" mostly (mostly) disappears once you move away from "paper as discovery" to "paper as one small piece of a large puzzle."

IMO, there is no such thing as a perfect study design. It is really rare that a study design can actually answer a question. Instead, those questions get answered by integrating and triangulating over many studies.