What would be the point of stopping them if you're doing the same thing?

I want to see more evo-devo in brain-inspired AI

Could it be that the cerebellum is a perfect area to study the duality of prediction and control?

But if going deeper means finding a connection to other theories describing that or other processes, it's hardcore theory-making, and deserves ultimate respect 🫡 2/2

I guess, what I mean is that if it's a deeper refinement of a theory that's focused on some particular process, personally, I'd like to see evidence that this theory is really good in describing that process before making that bet. 1/

Oh, definitely there are theories for theoreticians. I think, what distinguishes them is the scope - they are not about some specific idea/phenomenon, but a generalization of many ideas/phenomena that share some principles.

I think the point of the theory-experiment loop is that we don't really know which ideas are worth developing in detail unless we try to root ourselves in experiments. The ideas can be abundant, but most of them would be just ideas

Pride and superiority over reviewer 2 who could not see the obvious link between your past and current works

OR

Sadness from being old and not at your peak form anymore (which you were at when writing that previous paper)

Fully blind study

After several years of work, my lab is starting to put out our first papers on learning in a unicellular organism (Stentor coeruleus).

Here we show evidence for a form of associative learning in Stentor:
www.biorxiv.org/content/10.6...

What are these services? Networking?

Maybe "drawing parallels" was not the best wording, I didn't mean it as in "equate". I rather mean that we want to find what constraints are shared between nervous systems of different species, as well as what are the differences allowed within these constraints.

"Nothing in biology makes sense except in the light of evolution".
I come to think that we'd be very lucky if we manage to draw clear parallels between, let's say, the human brain and the mouse brain, without trying to follow their evolutionary history back to origins.

I don’t think AI’s success in coding will automatically translate to other fields. That level of performance only works where the output is as easily verifiable as code; and not many domains fit that bill. 2/2

New paper alert! 🚨

We found that the brain's compass is remarkably stable at two scales

1️⃣ the system maintains its internal organization for weeks
2️⃣ It "remembers" its orientation for weeks, even after a single visit

This may be key to how the brain aligns its other maps.

Paper: rdcu.be/e3waP

To rule out the relationship between cytoarchitecture and function, one needs to be sure they are trying to map correct functions. What makes people confident about it?

For one, they can help in shaping out what "brain-like" is

You can still find some link between some processes that nobody linked before. It probably won't be as fundamental as GR but being fundamental ≠ being important.

On a deeper level: the purpose of arealization is (or should be) be to help understand function; so function has no place on the list of area criteria.

If the other criteria disagree with function, that just makes them a poor guide to arealization.

More efficient than what?

The other side of the coin is that if the knob is used but doesn't serve any purpose, the usage has to stop for homeostatic reasons (and sometimes even the knob itself disassembled). So I guess, if it wasn't your intervention that turned the knob, assume that it serves a purpose.

And of course, not all the tracks will be on a new platform (I think I've lost about 1%).
But also some of the tracks that I liked before they disappeared from Spotify are restored in the new playlists.

I used TuneMyMusic for that, had to pay a few bucks to transfer my ~10k songs distributed over ~50 playlists.
So far I have caught just a few alien tracks in the new playlists

Now I'm curious what are the others

But the lack of respect for science can't be reduced to the uncertainty with which the findings are communicated. I would argue that one of the causes is the opposite - people too often see clickbait titles "Scientists discovered X!!!", dig deeper, get disillusioned, and figure that it's all a con

What made you want to switch?

The latter, you just wait for the next debate over how to think about the brain

It seems to me that being vulnerable to anti-science propaganda too often goes hand in hand with not being into sci-fi literature

This summer my lab's journal club somewhat unintentionally ended up reading papers on a theme of "more naturalistic computational neuroscience". I figured I'd share the list of papers here 🧵:

I wonder, where would be a good place to do modeling and chat with many people that study different species or do comparative studies? (asking for a friend)