What are the brain’s “real” tuning curves? . Our new preprint "SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour” argues that existing techniques for latent variable discovery are lacking. We suggest a much simpl-er way to do things. 1/21🧵

A great article from SWC on our neural data analysis technique (now accepted into ICLR). More updates to come soon. plus will be @CosyneMeeting presenting this too!.

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🌍🧠💻Applications are well and truly open for the third CaMinA. African nationals studying biology, medicine, engineering, maths etc. can, and should, apply for this summer school in Zambia. Neuro and ML are changing the world and now I the time to get into them, please RT!.

CaMinA is back for its 3rd year. this time we're going to beautiful Zambia🇿🇲!. I'm proud to see this course grow and bring together the smartest students across Africa with leading neuro institutes like @AllenInstitute and @SWC_Neuro. Applications open soon, please share widely!

RT @trend_camina: 🌍Exciting news! The 2025 TReND-CaMinA Course will be held in Lusaka, Zambia 🇿🇲 from July 7th–23rd. Dive into computationa….

At the risk of rambling I'll end the thread here and perhaps do a deeper dive in the future. Give it a read (or better, try it on your data) and let us know your thoughts! . 21/21.

This isn’t cheating, behaviour has always been there for the taking and we should exploit it. If we ignore behaviour and initialise randomly SIMPL still works but the latent space isn’t smooth and “identifiable”. This is certainly something to consider…. 20/21.

Initialising at behaviour is a powerful trick here. In many regions (e.g., but not limited to, hippocampus 👀), a behavioural correlate (position👀) exists which is VERY CLOSE to the true latent. Starting right next to the global maxima help makes optimisation straightforward.

These non-local dynamics aren’t a new discovery by any means but this is, in our opinion, the correct and quickest way to find them. 18/21.

And there’s cool stuff in the optimised latent too. It mostly tracks behaviour (hippocampus is still mostly a cognitive map) but does occasional big jumps as though the animal is contemplating another location in the environment. 17/21

Dubious analogy: Using behaviour alone to study neural representations (status quo for hippocampus) is like wearing mittens and trying to a figure out the shape of a delicate statue in the dark. Everything is blurred. 16/21.

The old paradigm of “just smooth spikes against position” is wrong! Those aren’t tuning curves in a causal sense…they’re just smoothed spikes. These “real” tuning curves (the output of an algorithm like SIMPL) are the ones we should be analysing/theorising about. 15/21.

It’s quite a sizeable effect. The median place cell has 23% more place fields. the median place field is 34% smaller and has a firing rate 45% higher. It’s hard to overstate this result…. 14/21

When applied to a similarly large (but now real) hippocampal dataset SIMPL optimises the tuning curves. “Real” place fields, it turns out, are much smaller, sharper, more numerous and more uniformly-distributed than previously thought. 13/21

SIMPL outperforms CEBRA — a contemporary, more general-purpose, neural-net-based technique — in terms of performance and compute-time. It’s over 30x faster. 12/21

Let’s test SIMPL: We make artificial grid cell data and add noise to the position (latent) variable. This noise blurs the grid fields out of recognition. Apply SIMPL and you recover a perfect estimate of the true trajectory and grid fields in a handful of compute-seconds. 11/21

I think this gif explains it well. The animal is "thinking" of the green location but located at the yellow. Spikes plotted against green give sharp grid fields but against yellow are blurred. In the brain this discrepancy will be caused by replay, planning, uncertainty and more.

behaviour =/= latent. This is obvious in non-navigational regions. But for HPC/MEC/etc. it’s definitely often overlooked…behaviour alone explains the spikes SO well (read: grid cells look pretty) it’s common to just stop there. But that leaves some error. 9/21.

In order to know the “true” tuning curves we need to know the “true” latent which passed through those curves to generate spikes. i.e. what was the animal thinking of…not what was the animal doing. This latent, of course, is often close to a behavioural readout such as position

So what’s the idea inspiring this? Basically, tuning curves (defined as plotting spikes against behaviour) aren’t the brains “real” tuning curves in any causal sense. But often we analyse and theorise about them as though they are. That's a problem. 7/21.