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A Erdem Sagtekin

@aesagtekin.bsky.social
729 followers 550 following 9 posts

theoretical neuroscience phd student at columbia

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Reposted by A Erdem Sagtekin
Matthijs Pals @matthijspals.bsky.social · 22h
What kind of dynamics underlie sequence working memory? In the last project of my PhD we developed and analysed RNNs fitted to multi-session single-unit data of macaques. Now out on: www.biorxiv.org/content/10.6... With @jakhmack.bsky.social and data from Chen et al., Neuron 2024. [1/4]
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Reposted by A Erdem Sagtekin
David G. Clark @david-g-clark.bsky.social · 31/07/2026
This September, I will join the @flatironinstitute.org Center for Computational Neuroscience as a Group Leader, where I will launch the High-Dimensional Dynamics & Computation Group. Information for postdocs and grad students is forthcoming. I am grateful to my mentors who made this possible!
Exterior view of the Flatiron Institute in Manhattan
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Reposted by A Erdem Sagtekin
Flatiron Institute @flatironinstitute.org · 30/04/2026
Congratulations to #FlatironCCN director @eerosim.bsky.social on his election to the National Academy of Sciences! bit.ly/42AEUhl #science #neuroscience
bit.ly
CCN Director Eero Simoncelli Elected to National Academy of Sciences
Eero Simoncelli, director of the Flatiron Institute’s Center for Computational Neuroscience (CCN), has been elected to the National Academy of Sciences.
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Reposted by A Erdem Sagtekin
Alex Williams @itsneuronal.bsky.social · 19/03/2026
Cosyne invited me to give a long tutorial (4 hours!) on methods to quantify differences high-d neural recordings across animals, brain regions, deep neural nets, etc. The recording is up on youtube. I hope it inspires more research on this fundamental topic! www.youtube.com/watch?v=n44x...
youtube.com
Cosyne 2026 - Cosyne Tutorial: Comparative Analysis of Neural Population Codes
YouTube video by Cosyne Talks
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Reposted by A Erdem Sagtekin
David G. Clark @david-g-clark.bsky.social · 04/03/2026
I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs: www.biorxiv.org/content/10.6...
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A Erdem Sagtekin @aesagtekin.bsky.social · 08/01/2026
1/7 How should feedback signals influence a network during learning? Should they first adjust synaptic weights, which then indirectly change neural activity (as in backprop.)? Or should they first adjust neural activity to guide synaptic updates (e.g., target prop.)? openreview.net/forum?id=xVI...
Diagram of a recurrent neural network: input goes into the network, output is compared to a target to produce an error, and dotted feedback arrows show updates to neural activity and to synaptic weights.
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Reposted by A Erdem Sagtekin
Owen Marschall @omarschall.bsky.social · 15/12/2025
1/X Excited to present this preprint on multi-tasking, with @david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...
biorxiv.org
A theory of multi-task computation and task selection
Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has...
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Reposted by A Erdem Sagtekin
Friedemann Zenke @fzenke.bsky.social · 27/05/2025
1/6 Why does the brain maintain such precise excitatory-inhibitory balance? Our new preprint explores a provocative idea: Small, targeted deviations from this balance may serve a purpose: to encode local error signals for learning. www.biorxiv.org/content/10.1... led by @jrbch.bsky.social
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Matthijs Pals @matthijspals.bsky.social · 11/12/2024
How to find all fixed points in piece-wise linear recurrent neural networks (RNNs)? A short thread 🧵 
In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7
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Reposted by A Erdem Sagtekin
David G. Clark @david-g-clark.bsky.social · 03/12/2024
(1/5) Fun fact: Several classic results in the stat. mech. of learning can be derived in a couple lines of simple algebra! In this paper with Haim Sompolinsky, we simplify and unify derivations for high-dimensional convex learning problems using a bipartite cavity method. arxiv.org/abs/2412.01110
arxiv.org
Simplified derivations for high-dimensional convex learning problems
Statistical physics provides tools for analyzing high-dimensional problems in machine learning and theoretical neuroscience. These calculations, particularly those using the replica method, often invo...
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A Erdem Sagtekin @aesagtekin.bsky.social · 09/11/2024
This list likely reflects mainly my interests and circle, and I’m sure I’ve missed many people, but I gave it a try: (I’ll be slowly editing it until it reaches 150/150) go.bsky.app/7VFUkdn (also, I tried but couldn't remove my profile...)
go.bsky.app
Comp Neuro Starter Pack
Join the conversation
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A Erdem Sagtekin @aesagtekin.bsky.social · 01/11/2024
i enjoyed reading the geometry of plasticity paper and felt that something important was coming, this is it:
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