Or at least give your definition in the introduction. I always try to...

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I agree. Useful tools.

But doesn't it feel different for you reading about something that is new for you, e.g. in an encyclopedia, or figuring it out for yourself?

(and compared to LLMs, encyclopedias were mostly right...)

Ok.

But is discovery the same without the process? Won't it become boring, when a machine does it for you? And maybe even predictable (and full of mistakes)?

I disagree. To me science is more about the process. A bit like art. Like creativity is not defined by the outcome, but by going through the process. What is science without the struggle? Without the human interaction? To me the knowledge, without the way would be meaningless.

Shoutout to my co-authors Johannes, Ohad, Jonas, Matthias, and Simon (all not on Bluesky, to my knowledge). It was great fun working with you!

You might ask: But where does representational drift come in?

If cognitive maps themselves change over time, they may continuously reshape the space of possible ideas. Thus opening new paths for exploration and enabling novelty.

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4/4

But navigation alone is not enough.
Creative thought also relies on variation and selection: generating candidate ideas and selecting those that are useful or valuable.

This connects creativity to core biological principles.

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3/4

A key ingredient: cognitive maps.
Ideas are structured in internal representations, and creativity involves navigating these maps, exploring remote associations and linking distant concepts.

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2/4

Towards Circuit Mechanisms of the Creative Process Creativity stands as one of the most intriguing aspects of cognition, attracting cross-disciplinary investigation due to its multifaceted nature and profound implications. While significant progres...

And now for something completely different...

New paper out: Towards circuit mechanisms of the creative process
doi.org/10.1080/1040...

How does the brain generate creative ideas?
We argue that creativity can be understood at the level of neural circuits - not just behavior or cognition.

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1/4

Recent studies have shown (e.g. Noda et al., 2025) that representational similarity seems to be preserved during representational drift.

Does this qualify? Maybe representations as in "representation of similarities" (Edelmann, 1998).

I can only recommend @bernsteinneuro.bsky.social conference. Topics similar to cosyne, but feels so much nicer.

Most recent models explain "representational drift" as continuous learning in some way (e.g. doi.org/10.1038/s415..., doi.org/10.7554/eLif... or our recent doi.org/10.1073/pnas...). I don't like the name "representational drift" either, but I'm afraid, it's here for good...

While I agree that the paper is beautiful (and some aspects are remarkably stable), a median decoding error of nearly 90° after 25 days (Fig. 1k) or a |Δ| preferred direction of 45° after 4 weeks hardly suggests that there is no drift. 😄

In the model, reproducing the empirical signal correlation → noise correlation relationship requires two things:

- A Hebbian component
AND
- A stochastic process

Drift emerges from the interplay between the two.

🧠🧪 9/9

A scientific figure, showing that we can reproduce the described predictive effect of signal on noise correlations. But only via a combination of Hebbian plasticity and a stochastic process.

Fig. 6: Modeling the mechanism

Finally, the model!

So, we see Hebbian structure in the data. But is a Hebbian mechanism enough to explain the observed drift?

No.

🧠🧪 8/9

One idea:
During intense learning, the balance shifts, perhaps to allow for consolidation, so that representations can reorganize or transfer to other areas of the brain.

🧠🧪 7/9

A scientific figure, showing how during fear conditioning the stabilizing effect of signal correlation on noise correlation is diminished.

Fig. 5: Fear conditioning decreases Hebbian signature

During fear conditioning, the signal correlation → noise correlation relationship is dampened.

The Hebbian plasticity is weakened. During learning!

Why might that be?

🧠🧪 6/9

A scientific figure, showing how noise correlation stability between consecutive imaging time points is growing with noise correlation on the first imaging time point. Vice versa there is no such effect.

Fig. 4: Signal correlation stabilizes noise correlation

Not only do signal correlations predict future noise correlation, they also predict noise correlation stability between t and t+1.

Stronger signal correlation → more stable noise correlation.

🧠🧪 5/9

A scientific figure, showing that signal correlations at a given time point are predictive of noise correlations at a later time point (2 days apart). Vice versa this effect is very small.

Fig. 3: Hebbian plasticity during drift

Here’s the first big result:

👉 Signal correlations at time t predict noise correlations at time t+1.

If two neurons co-activate now,
their future functional coupling rises.

This is the classic:
“Fire together → wire together.”

🧠🧪 4/9

A scientific figure, showing stable distributions of signal and noise correlations, but volatility of individual signal and noise correlations between imaging days1 and 3.

Fig. 2: A volatile steady state

Both signal and noise correlations appear to be in a stable distribution across days…

BUT on the level of individual pairs, both are highly volatile.

So at the population level it looks stable, yet at the pairwise level it’s highly dynamic.

🧠🧪 3/9

A scientific figure, describing how we computed signal and noise correlations.

Fig. 1: Defining SC and NC

- Signal correlations (SC): co-active cells
- Noise correlations (NC): functional connectivity

For the SC/NC aficionados:
We compute SC from the median response and estimate both SC and NC via bootstrapping.

👉 At a single time point, SC and NC are uncorrelated.

🧠🧪 2/9

As promised: a detailed figure-by-figure thread on our @pnas.org paper:

doi.org/10.1073/pnas...

We use signal correlations and noise correlations in chronic imaging data to show that representational drift is shaped by a balance between Hebbian and stochastic changes.

Let’s dive in 👇

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Yes. We figured that quite a bit of this relation in the literature might stem from spurious correlations.

Sorry for not referencing your work. We might have used your method, if we had been aware of it. We discussed the SC / NC bias a lot and struggled quite a bit to find a solution.

Yes. Sorry, we had to shorten significantly in the end, so the detailed methods are only in the supplement.

Thanks a lot. This is very interesting.

We did not split the data into odd and even trials to compute signal correlations, but we subsampled random trials and averaged. So, we followed a similar approach. And in the end we see no correlation at all between SC and NC (within one imaging session).

Huge shoutout to all co-authors ❤️
And especially to co-first author Thomas - it was amazing to work with you on this project!

I will have a figure by figure thread on Sunday or early next week. If anyone has questions let me know so I can answer them then.

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5/5

But Hebbian learning alone isn’t enough. In a computational model, we find that to reproduce the observed drift, we also need a stochastic process, either in the inputs or in the network itself.

Representational drift emerges from a balance between stochastic changes and Hebbian learning.

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4/5

We find: during representational drift, SC at one time point predicts NC at a later time point, exactly what to expect during Hebbian learning.

Representational drift is not just passive instability.
It reflects ongoing Hebbian plasticity continuously reshaping effective connectivity.

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3/5

We analyze population recordings and use

• signal correlations (SC) as a proxy for co-active neurons and
• noise correlations (NC) as a proxy for effective connectivity

to track how activity and connectivity co-evolve over time during representational drift.

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2/5