I love open science! Not only is this absolutely brilliant work on how time and events are encoded in LEC, it is also a crazy rich dataset and it's just available to everyone! Thank you so much for sharing the recordings, I can't wait to play around with it!

Very proud to have presented our work on modeling cognitive maps and episodic memory at the GEM conference today! @for2812.bsky.social

Check out our preprint to learn more about how you can bring together semantic and spatial information with our grid-cell-VSA model arxiv.org/abs/2503.08608

Very excited to get the opportunity to give a talk at GEM2025 on how we combine spatial and semantic information to form episodic memories. See you in beautiful Bochum :)

Very proud to announce the first paper for my PhD:
Grid Cell-Inspired Vector Algebra: Bridging the Brain's Navigation System with Symbolic Reasoning. Unifying spatial and symbolic computation!

Happy to be in Heidelberg next week, presenting this at NICE.
arxiv.org/abs/2503.08608
#NICE2025

Curious about mapping neuromodulators (dopamine, serotonin, etc.) to hypernetworks. The two concepts seem very related (synaptic modulations via neuromodulators and task dependent weight adaptation in hypernetworks). Any existing work in that area? Would love pointers to papers exploring #ml #neuro

I was curious about the wonky AI overview results being delivered by Google search, so I looked at this a bit further.

"What is heavier: an elephant or an elephant with an ant on its back?"

Re-reading classic papers from the 1940s, I have to say... introductions were different back then

Some of my thoughts on OpenAI's o3 and the ARC-AGI benchmark

aiguide.substack.com/p/did-openai...

OK If we are moving to Bluesky I am rescuing my favourite ever twitter thread (Jan 2019).

The renamed:

Bluesky-sized history of neuroscience (biased by my interests)

there is actually a really nice blogpost by the numenta people illustrating how multiple modules create a unique position encoding although every module repeats with some scale and orientation
www.numenta.com/blog/2018/05...

The key point is that you don't wrap the whole room into a torus. It's not the length and width of the room that tells you the periodicity on your torus. Rather you have multiple Tori, say one that wraps every 3 meters and another that wraps every 5 meters and so on.

Why would they expect that? The periodicity is not defined by the width of the room and different grid cell modules have different periodicities (scales and orientations), therefore the whole population encoding is not at all periodic at the borders of the environment

I don't really see the prediction part. Isn't it pretty safe to assume that the real papers in the database are already in the training set for all of these LLMs? In that case it is more of a remembering task than a prediction, right?