another year and another @trend_camina summer school underway! Our fourth! Always fun learning and sharing computational neuroscience with others, this year in Nyeri, Kenya🇰🇪 Special thanks to @IVADO_Qc , @Mila_Quebec and @ai_unique for support!

Great work and a relevant read for neuroscientists and machine learners alike. Made me fundamentally reconsider how I thought about layer normalisation! Well done @RoyEyono

A little overdue but happy to announce that I 1) Got my PhD🎓 2) Started a postdoc and CNS Fellowship with Guillaume Lajoie and Blake Richards (@g_lajoie_ @tyrell_turing) Come find me at @Mila_Quebec in Montreal working on new things NeuroAI!

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!

Application deadline has been extend. Two more weeks to apply...don't miss out!!

🌍🧠💻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!

amazing!!!!

loving the hype

((also I'm not saying it shouldn't still be used - in many cases its still quite useful...just that perhaps we could look a bit deeper))

I think we should! (my experience with a lot of contemporary work in HPC is that tuning curves == spikes fitted to behaviour is still dominant)

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!

nice I hadn't seen this one! Muller and Kubie 1989 did the same thing with place fields. Was an inspiration for SIMPL (which is like an automatic and much less constrained version of this idea)

Hi Ben, this is exactly the plan so thanks for bringing the @DANDIarchive datasets to my attention. I'd love your help here! We should chat...

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

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