Neuroscience/ML PhD @UCL. NeuroAI, navigation, hippocampus
London
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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!
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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
How do neural circuits in the brain implement normalization? 🧠
In our new paper, we show that just normalizing sensory input isn't enough. Crucially, we must also normalize the error signals! 🧵👇
Paper: arxiv.org/abs/2603.17676
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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!
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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!
How does the brain represent imagined locations?
Researchers at SWC, @GatsbyUCL and @UCL developed SIMPL, a method to refine neural tuning curves by correcting distortions from imagined locations—sharpening our view of place cell activity.
Read more: sainsburywellcome.org/web/bl…
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Application deadline has been extend. Two more weeks to apply...don't miss out!!
Good news!🎉 The application deadline for TReND-CaMinA has been extended to ❗️31st January❗️
Don’t miss this chance to boost your computational neuroscience journey and become part of our community🧠✨
Apply now: trendinafrica.org/trend-cami… #Neuroscience #Education #ScienceForChange
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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!
🎉Happy New Year! Start 2025 by investing in your future!🚀
Just 2 weeks left to apply for our Computational Neuroscience & Machine Learning course🧠🤖
Let’s make this year one for growth & discovery. RT to spread the word!🙌
🔗trendinafrica.org/trend-cami…
#Growth #Opportunity
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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!
🌍Exciting news! The 2025 TReND-CaMinA Course will be held in Lusaka, Zambia 🇿🇲 from July 7th–23rd. Dive into computational neuroscience and machine learning with us!
📅 Applications open: December 15th
🔗More info: trendinafrica.org/trend-cami…
Stay tuned & spread the word! 🧠✨
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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)
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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...
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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