Actually, according to an eLife information session, eLife desk reject ~90% of submissions.

So the ones which do get reviewed tend to be high-quality!

Even if, like the paper @neural-reckoning.org is referring to, the reviewers don't agree with everything.

Excited to read!

You may be interested in our recent work too!

We define 128 RNN architectures then compare their behaviour and dynamics (using gradient-based metrics).

bsky.app/profile/marc...

For the two I've held at Imperial, applications will open summer 2026, for a September 2027 start.

* AI in Science (2 years) - www.imperial.ac.uk/ix-ai-in-sci...
* ICRF (4 years) - www.imperial.ac.uk/research-and...

I'm always open to talking about postdoctoral fellowships!

I'm not quite ready to post my applications online, but am happy to share them privately.

Just DM or email me 🙂

@neural-reckoning.org and I defined a taxonomy of neural network architectures, which we exhaustively explored!

We think these models (pRNNs) are exciting for thinking about structure-function relations in artificial and biological neural networks.

bsky.app/profile/marc...

Congratulations!

Hopefully see you somewhere in Europe soon!

For physical systems yes (from hydraulics to computers)!

But, I don't remember seeing an algorithm-centric version?

Congratulations!

Exciting to see an inversion of NeuroAI too

Really neat!

It could be interesting to explore heterogenous neuromodulation?

In this case, having multiple modulator networks which act at different timescales and exert different effects on the downstream SNN?

This would be a bit closer to neuromodulators in vivo.

Congratulations!

What I was trying to highlight is that it is possible to observe low dimensional structure in population activity which is unrelated to the computation / task.

And it's not obvious that this problem vanishes in more complex circuits?

I think by adding a mixture of noise, delays etc, we may not end up at d=1.

But even if we did, many studies would describe these "population dynamics" as a line attractor.

Rather than just a single neuron acting on it's own?

Its good to see that there are causal studies.

But, these seem to be the exception?

And perhaps the language used in the non-causal studies can be a bit misleading?

For instance, the post that launched this thread claimed that "the brain uses distributed coding" (based on solely on recordings).

What I was trying to highlight is that it is possible to observe low dimensional structure in population activity which is unrelated to the computation / task.

And it's not obvious that this problem vanishes in more complex circuits?

The toy circuit is very simple. But I'd be happy to say it computes?

For example, given LIF dynamics it could perform temporal coincidence detection (only outputting spikes in response to inputs close together in time).

I think by adding a mixture of noise, delays etc, we may not end up at d=1.

But even if we did, many studies would describe these "population dynamics" as a line attractor.

Rather than just a single neuron acting on it's own.

Causal experiments could help to untangle this.

But, it seems like this remains underexplored?

Keen to hear your thoughts @mattperich.bsky.social, @juangallego.bsky.social and others!

www.nature.com/articles/s41...

🧵5/5