iiinova — where your digital self keeps evolving

Great question. Local optima will always be hard to identify. Ofc if you have a reason to believe behaviour really _isn't_ a good initialisation then you shouldn't use it. You can always / we already track the log-likelihood of held-out spikes. If this increases then things are looking good.

120

you were right though....the grass is greener over here ;)

110

SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour

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! tomge.org/papers/simpl/ 21/21

tomge.org

An efficient technique for optimising tuning curves starting from behaviour by iteratively refitting the tuning curves and redecoding the latent variables.

230

This isn’t cheating, behaviour has always been there for the taking and we should exploit it (many techniques specialise in joint behavioural-neural analysis). If we ignore behaviour SIMPL still works but the latent space isn’t smooth and “identifiable”...certainly something to consider. 20/21

120

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

120

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

150

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. It also beats pi-VAE and GPLVM. 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

230

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

SIMPL is also "identifiable", returning not just any tuning curves but specifically THE tuning curves which generated the data (there are some caveats / subtleties here) 6/21

230

SIMPL is very cheap to run (30x faster than CEBRA). Why? Because we design it to be: • Fitting: we use spike smoothing (strictly KDE) • Decoding: we use Kalman-smoothed MLE. No neural nets or Gaussian processes to slow it down. Optimised in JAX. It has only 2 hyperparameters.

This works because it’s really just expectation maximisation (EM) — a well known algo for optimising models with hidden latents — with some faff removed. We show how, under relatively weak assumptions, repeated redecoding (SIMPL) is the same thing. 4/21

TL;DR 1. Start by assuming the latent IS behaviour (e.g. the animals position) … 2. ...fit tuning curves to this latent… 3. …re-decode the latent from these tuning curves… 4. …repeat steps 2 and 3 You will, very quickly, converge on the “true” latent space.

All details, links, code etc. can be found here: tomge.org/papers/simpl/ And a huge shout out to my amazing collaborators Pierre Glaser as well as @clopathlab.bsky.social, Kim Stachenfeld and @caswell.bsky.social ! 2/21

141

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🧵

47222

Epic data and representational analysis work by @jquinnlee.bsky.social on CA1, now out in Neuron! Highly recommend people taking a look (+ amazing visuals, these flow diagrams are fascinating to look at!)