Job alert! We are seeking a rodent in vivo electrophysiologist to work on coordination between anterior thalamus and hippocampus in rats using Neuropixels. Beautiful Scotland, leading university (Glasgow) and vibrant intellectual setting. Post is 23 months but hopefully extensible. Details here 👇
Can we really measure replay in humans using MEG with current methods? In our most recent paper we simulated replay under realistic conditions via a novel hybrid approach with astonishing results.
we're delighted that it has now been published @elife.bsky.social!
elifesciences.org/articles/108...
New paper from the Neural Computation Group: "A theory of subicular function and generalized vector coding." by @ffeiwang.bsky.social. Link to preprint and a quick overview in Fei's short thread.
Arguably spending "years of my training learning to translate scientific thinking into models and code" is part of what made you a capable.
Question is, does something go missing when one relies on AI from the get-go? This article sums that notion up pretty well.
ergosphere.blog/posts/the-ma...
New book on navigation (open access). The result of the Ernst Strüngemann Forum 2024. Was great to be there and discuss for a week with 50+ wonderful colleagues. Many thanks to the organizers, Julia Lupp + ESF team, the editors
@noranewcombe.bsky.social, Ken Cheng
link.springer.com/book/10.1007...
Wait… localized norepinephrine transients in the awake visual cortex?!
Who would have guessed this neuromodulatory signal is that spatially precise, right where visual processing is happening. Brain state control just got a lot more local. @ruedigersarah.bsky.social www.nature.com/articles/s41...
"The digital sphinx produces highly realistic fly walking - yet it is biologically meaningless. This exercise teaches us nothing about either animal and exposes a core peril of connectome-body models: behavioral fidelity is achievable without biological fidelity..."
www.biorxiv.org/content/10.6...
1/7 🧠 My journey into development begins with this work and question: how does the brain's spatial navigation system develop? We found that the neural networks for spatial navigation (tori and rings) are preconfigured and only later anchor gradually to the world with experience! 🧵
Is spatial navigation innate 🧠? Using #NeuroPixels we show that the #torus 🍩 underlying the #GridCell map exists already on day 10 in rats — before pups open eyes and ears and before they start upright walking. 🧵1:4
👇
www.biorxiv.org/content/10.6...
Sure. Maybe we can have a brief chat at Cosyne.
19/N: Finally, you’ll all be glad to hear this is – for once – a short paper ;). I hope people find it interesting.
19/N: Massive thanks to @doellerlab.bsky.social at @mpicbs.bsky.social for the scientific environment in which these ideas could mature, and to @vigano.bsky.social, @stephanie-theves.bsky.social, @vstudenyak.bsky.social, @keckjanis.bsky.social, and my group for discussions.
18/N: The reliance on extant spatial navigation architectures also means we get all the machinery of spatial memory for free. Replay, remapping, the role of theta, etc. Post learning adjustment … though not modeled here.
17/N – D3: The model works with discontinuous stimulus spaces (OASIS being positive proof that this is needed) but would also be compatible with morphing/continuous navigation in abstract space. In the BB model paper @NeilBurgess10 and I proposed a model of mental navigation that could be used.
16/N - D2: I see this as in line with Eichenbaum & Cohen’s (also articulated by others) conjecture that we can unify the spatial navigation view of the hippocampal formation with the memory view. The present model makes this explicit for abstract cognition.
15/N: A bit of mini discussion - D1: Here grid cells are not latent variables in an ML architecture, but rather pre-existing, re-purposed from spatial navigation with minimal (plausible) assumptions (like velocity scaling).
14/N: What I personally find satisfying here: the same mechanisms that are used to build the map (VNA and PIN) are the ones that subsequently operate on it to implement reasoning (e.g., the analogies above). No additional machinery. Different combination for different operations that could transfer
13/N: Similarly we can combine PIN and VNA in various ways to compute the key quantities for perspective taking in abstract space (similar to the 2023 study by @vigano.bsky.social) or Subspace construction. Like a composable set that could transfer. More types of reasoning outlined in the paper.
12/N: Now the fun part. Once we have anchored stimuli to the map we can do abstract reasoning. Say we have grid cell PVs for points A and B and the displacement vector. Then we pick a random point C. Do PIN(C,d) = D. Now we know C is to (a stimulus near) D as A is B. We got an analogy.
11/N To check that mapping is successful, we recover grid cells from memorized PVs and bin them. Recovered grids are a proxy for what one would record. This works if, and only if, the mapping respects stimulus relations. Scrambling similarities breaks these recovered grids, as it should.
10/N: If you can't agree, refuse to anchor the stimulus. This implies a very straight-forward (and reasonable) behavioral prediction: hard tasks take longer in the model. Too difficult a task can never be accomplished in a limited time.
9/N: Using multiple anchors and overlap is powerful and can be extended. This gives us fault tolerance! If stimuli are hard to map, do more triangulations and take a majority vote or average at the end. Then anchor the next stimulus.
8/N: The PIN takes in S1 and d to infer the grid cell PV at which to anchor S2. Then proceed onwards with the rest of the stimuli. We can also infer S3 from two locations, e.g. with anchor S1 and with anchor S2, and then check for overlap in the inferred GC PV.
7/N: Then we map. Pick a random next stimulus S2. Calculate the similarity in stimulus space (e.g. valence and arousal for OASIS, leg and neck length for birds), rescale it to within the range of the grid cell network, which gives us the displacement vector.
6/N: How the relevant dimensions could be isolated is also sketched out in the article, but I focus on the mapping. How does that work? We take a starting grid cell PV and anchor a first stimulus S1 (e.g., a specific OASIS image) to it. S1 <-> GC PV.
5/N: I test the model with the public OASIS dataset, also used by @lukaskunz.bsky.social and colleagues to directly record non-spatial grid cells. I also generate some stretchy birds, akin to seminal study by Alexandra Constantinescu and @behrenstimb.bsky.social
4/N: Two key hypotheses: First, grid scaling is plastic, as shown by @caswell.bsky.social and used in my prior model of visual grid cells (BicanskiBurgess2019). Second, similarity in stimulus space is quite literally distance. That brings me to the stimuli.
3/N: For methods details, see the article. What happens in the model: Point A is a grid cell population vector (PV), B is also signaled by a GC PV. d is the relative displacement vector. The same d connects many point pairs, and the PIN implementation accounts for that.
2/N: VNAs can take two points A and B and spit out the vector connecting them. Beautifully explored by Bush et al. 2015, very useful for spatial navigation, but to build UCMs we need the opposite. Take in A and the vector d, and spit out point B. That would be the PIN.
1/N: Dear cognitive map fans, I’d like to share a model I’ve been working on for a while (clearing backlog :). I show how a vector navigation architecture (VNA) and a “positional inference network” (PIN) can build Universal Cognitive Maps (UCMs) for abstract spaces.
www.biorxiv.org/content/10.6...
