Samuel Eckmann
@samueleckmann.bsky.social
710 followers 300 following 77 posts
Computational neuroscientist, currently in Cambridge, UK (CBL) as a Newton International Fellow. Interested in neural circuits, E-I balance, and biological learning → http://samueleckmann.github.io
Posts Media Videos Starter Packs
Reposted by Samuel Eckmann
sfnjournals.bsky.social
#JNeurosci: Kashefi et al. dissociate between the “what” and “how” components of motor sequence learning and provides evidence for the development of motoric sequence representations that guide optimal movement execution.
https://doi.org/10.1523/JNEUROSCI.0299-25.2025
Reposted by Samuel Eckmann
laurelinelogiaco.bsky.social
Interested in doing a Ph.D. to work on building models of the brain/behavior? Consider applying to graduate schools at CU Anschutz:
1. Neuroscience www.cuanschutz.edu/graduate-pro...
2. Bioengineering engineering.ucdenver.edu/bioengineeri...

You could work with several comp neuro PIs, including me.
Reposted by Samuel Eckmann
rhythmicspikes.bsky.social
1/
🚨 New preprint! 🚨

Excited and proud (& a little nervous 😅) to share our latest work on the importance of #theta-timescale spiking during #locomotion in #learning. If you care about how organisms learn, buckle up. 🧵👇

📄 www.biorxiv.org/content/10.1...
💻 code + data 🔗 below 🤩

#neuroskyence
Reposted by Samuel Eckmann
apeyrache.bsky.social
« Diverse calcium dynamics underlie place field formation in hippocampal CA1 pyramidal cells. »

A fundamental study now published @elife.bsky.social

elifesciences.org/reviewed-pre...
Reposted by Samuel Eckmann
jmxpearson.bsky.social
Didn’t want to hijack someone else’s thread, but also: what’s the alternative? Sure, evolution produces sufficient not optimal solutions. Cool. How do we model that? Or if you don’t care about modeling, what predictions does that make that we can test experimentally?
jmxpearson.bsky.social
I mean, efficient coding and optimal control work *far* better than they have a right to, and they generate predictions. They are not falsifiable hypotheses except in the narrowest sense, but they are programs that can identify parsimonious principles for explaining real observations.
Reposted by Samuel Eckmann
melaniemitchell.bsky.social
Eliezer Yudkowsky, quoted in the NY Times.

For anyone who has followed the "AI Safety" / "Less Wrong" discussions, this is...well, something.
samueleckmann.bsky.social
Everything Everywhere ...
intlbrainlab.bsky.social
Two flagship papers from the International Brain Laboratory, now out in ‪@Nature.com‬:
🧠 Brain-wide map of neural activity during complex behaviour: doi.org/10.1038/s41586-025-09235-0
🧠 Brain-wide representations of prior information in mouse decision-making: doi.org/10.1038/s41586-025-09226-1 +
Reposted by Samuel Eckmann
samueleckmann.bsky.social
true, but also easier said than done 😬
samueleckmann.bsky.social
„it is important that the theorist’s
tendency towards reductionism does not cloud out the complexity
of the living brain“
antihebbiann.bsky.social
I wrote a Comment on neurotheory, and now you can read it!

Some thoughts on where neurotheory has and has not taken root within the neuroscience community, how it has shaped those subfields, and where we theorists might look next for fresh adventures.

www.nature.com/articles/s41...
Theoretical neuroscience has room to grow
Nature Reviews Neuroscience - The goal of theoretical neuroscience is to uncover principles of neural computation through careful design and interpretation of mathematical models. Here, I examine...
www.nature.com
Reposted by Samuel Eckmann
gerstnerlab.bsky.social
Is it possible to go from spikes to rates without averaging?

We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed!

Presented at Gatsby Neural Dynamics Workshop, London.
From Spikes To Rates
YouTube video by Gerstner Lab
youtu.be
Reposted by Samuel Eckmann
moraogando.bsky.social
Thrilled to share our new Adesnik lab paper!!
Using holography in excitatory & inhibitory neurons, we reveal how a single cortical circuit can both complete and cancel predictable sensory activity, sharpening representations
📄https://www.biorxiv.org/content/10.1101/2025.08.02.668307v1
🧵
Reposted by Samuel Eckmann
k4tj4.bsky.social
1
To predict the behaviour of a primate, would you rather base your guess on a closely related species or one with a similar brain shape? We looked at brains & behaviours of 70 species, you’ll be surprised!

🧵Thread on our new preprint with @r3rt0.bsky.social , doi.org/10.1101/2025...
Brain Surfaces of 70 primate species
Reposted by Samuel Eckmann
maxplanck.de
Director at Max Planck - a unique position! The Open Call for Expressions of Interest in Max Planck Directorships is open now and can be submitted by the 31st of October 2025. ➡️ mpg.de/directors - Please share the Open Call among potential candidates.
Open Call for Expressions of Interest in Max Planck Directorships:
Expressions of interest can be submitted until 31 October 2025.
Reposted by Samuel Eckmann
biorxiv-neursci.bsky.social
Feature-specific inhibitory connectivity augments the accuracy of cortical representations https://www.biorxiv.org/content/10.1101/2025.08.02.668307v1
samueleckmann.bsky.social
What do you have in mind as evidence that speaks against this? Generally broadly tuned inhibition?
samueleckmann.bsky.social
'Unlike in artificial neural networks, learning in biological networks is thought to modify synaptic weights but not signs ... here, we demonstrate experience-dependent sign switching at glutamate and GABA co-releasing synapses'
samueleckmann.bsky.social
This is a nice history lesson on inhibitory plasticity. Not sure I agree about the present and future though ;)
tpvogels.bsky.social
I now had a chance to listen to it and it’s not entirely terrible. Gaute’s voice is actually quite soothing and his questions are good. You might not immediately die while listening to this. Thanks @gauteeinevoll.bsky.social for cutting out all the bits that would’ve gotten me fired.
gauteeinevoll.bsky.social
Episode #30 in #TheoreticalNeurosciencePodcast: On co-dependent excitatory and inhibitory plasticity – with Tim Vogels @tpvogels.bsky.social

theoreticalneuroscience.no/thn30

How can excitatory and inhibitory synaptic plasticity, co-exist in spiking neural network models? With co-dependence!
Reposted by Samuel Eckmann