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

However,

In this case, from the anatomy (circuit diagram above), we know that only neuron_1 is involved in the computation (transforming the input to the output).

And the manifold we observe is misleading.

🧵4/5

A common approach to analysing this data would be to apply PCA (or another technique).

Yielding a matrix of population activity (d x time). Where d < the number of neurons.

A common interpretation of this would be that "the brain uses a low dimensional manifold to link this input-output".

🧵3/5

If we start from this circuit:

Input -> neuron_1 -> output
↓
⋮
↓
neuron_n

And record neurons 1 to n simultaneously (where n could be very large).

We can obtain a matrix of neural activity (neurons x time).

🧵2/5

Okay, let's say we make the circuit above more realistic: add neurons, separate them into areas, consider multiple tasks etc.

The problem outlined above still persists?

If a method doesn't work in a simple system (e.g. the one above), there is no reason to think it will in a more complex one?

But what does the lag tell you about the system?

Taking the circuit above as an example.

There may be a difference in the lag between the input signal and neurons 1 and 2.

But this difference could be too small to be detectable?

And without causal experiments the conclusion could be something like "different neurons represent the stimulus at different timescales"?

🧵 (3/3)

Input -> neuron_1 -> output
↓
neuron_2

In this "circuit":
* We could decode the input from either neuron
* But the circuit is not "using" neuron_2 in the computation (transforming the input to output).
* And ablations would make this clear?

🧵 (2/3)

I agree that perturbations have their challenges (see doi.org/10.1371/jour... from @kayson.bsky.social for a better approach).

But without them you just have correlations?

Just to give a toy example:

🧵 (1/3)