At the very least, it is completely superfluous and can just be omitted.

How, then, would the same stimulus within one session result reliably in the same neuronal response?

Did you watch the video? Or read the article?
I have the feeling, you're talking about something completely different. We are talking "representational drift". Not movement detection. How would movement detection account for different responses to the same stimulus on different days?

Direct link to the video 🎥
youtu.be/z63fmYSBcB0

And the excellent article the outreach team wrote for the CRM homepage:
www.crm.cat/why-your-bra...

@crmatematica.bsky.social
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I’ll address this question in a minimal model, which I’ll present at the Bernstein Conference (29 Sep – 02 Oct) in Frankfurt.

If you’re interested, come to our workshop or visit my poster. Looking forward!

@bernsteinneuro.bsky.social
@crmatematica.bsky.social

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This is the first paper to come out from my postdoc at @crmatematica.bsky.social.

Still in collaboration with my old friends Simon Rumpel and Matthias Kaschube, though. 🧠🤝

5/5

This process can be likened to "herding cats": the stochastic changes (cats) are kept in check by statistical learning (shepherd & dogs). We even added an illustration containing 🐱 & 🐶. 🧠🧪

4/5

We propose: ongoing statistical learning is not only for adapting to new environments, but also to continuously maintain memories in the face of stochastic changes.

Without it, the system would lose track. With it, the cognitive map stays intact. 🔄🧠

3/5

This means that what we call “representations” (Edelman, 1998: "Representation as representation of similarities") are actually stable during “representational drift” - even though neuronal activities change. 🧠🔍

A stable percept can thus emerge from an unstable substrate.

2/5

I like the freedom to follow my own ideas and availability to get feedback on those ideas.

Late postdoc stage, though. During my PhD I liked more and closer guidance better (in exchange for less freedom).

I hope, it is not always 1:1 the environment. :D If the brain would only reflect the environment, how would we be able to learn at all?

I am really excited about all the evidence on learning and manifolds. So much still to be found and understood.

And for me the big question is: What about representational drift? 😄

Manifolds can be mapped, but individual neuronal responses change. That’s neat, but how does it make sense?

How can perception stay stable if the neurons keep changing?
I’m sure manifolds will help us understand that too…

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3/3

Brain structure and behavior give rise to manifolds.

But what about the environment? Either through statistical learning or just similar inputs leading to similar representations: the input/task will shape the manifold, no?

Linear track → linear representation.
Circular color space → ring.

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2/3

:D

I do think excitability plays a role and I really like the tuning changes through lateral inhibition.

I'm not sure about the significance of excitability, as already minor synaptic weight changes can cause huge response changes. And synaptic weights change considerably.

:D Ich habe das Zeichenlimit bis auf das letzte Zeichen ausgenutzt...