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Reposted by Jascha Achterberg

Martin Schrimpf @mschrimpf.bsky.social · 9d

Come be our colleague at EPFL! Several open calls for positions 🧪🧠🤖

* Neuroscience www.epfl.ch/about/workin... (deadline Oct 1)

* Life Science Engineering www.epfl.ch/about/workin...

* CS general call www.epfl.ch/about/workin...

* Learning Sciences www.epfl.ch/about/workin...

Faculty Position in Neuroscience

The School of Life Sciences at EPFL invites applications for a Tenure Track Assistant Professor position in Neuroscience. At EPFL researchers develop and apply innovative technologies to understand br...

www.epfl.ch

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Reposted by Jascha Achterberg

Ladislas Nalborczyk @lnalborczyk.bsky.social · Sep 8

Statistical methods for dissecting interactions between brain areas

www.sciencedirect.com/science/arti...

Statistical methods for dissecting interactions between brain areas

The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals…

www.sciencedirect.com

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Reposted by Jascha Achterberg

Grace Lindsay @neurograce.bsky.social · Aug 24

ATTN🚨: I will be looking for PhD students through NYU's Center for Data Science PhD program this year. Applicants should have an interest in either NeuroAI (specifically biological attention or AI interpretability) or ML for Remote Sensing. Visit my lab website for more info: lindsay-lab.github.io

PhD in Data Science: Admissions Requirements | NYU CDS

Discover the PhD in Data Science requirements at NYU. Learn about deadlines, required degrees, coursework, and application details for Fall 2025 admissions.

cds.nyu.edu

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Jaan Aru @jaanaru.bsky.social · Aug 25

Could we understand vision as a type of problem-solving? In this new paper, we develop a computational model that iteratively refines the hypothesis about the visual input with evolutionary search.

www.biorxiv.org/content/10.1...

work led by @tarunkhajuria.bsky.social

#visionscience #neuroAI

The difficult constellation image is solved by generating candidate solutions with a GAN and refined using a genetic search conditioned on best fitting of the solution outline to the dots on the constellation image.

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Reposted by Jascha Achterberg

Blake Richards @tyrellturing.bsky.social · Aug 20

This is really funny…

Ed Zitron @edzitron.com · Aug 18

The FT has been consistently good on this stuff, the only outlet to really take coreweave seriously, had great reporting, probably my fav business outlet. The Altman oil piece by bryce elder is one of the most devastating things I've ever read

www.ft.com/content/b180...

Three things we learned about Sam Altman by scoping his kitchen

All Drizzle, no sizzle

www.ft.com

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Jascha Achterberg @achterbrain.bsky.social · Aug 20

I find your point about probabilistic definition interesting -- never seen such a definition of it, but that could neatly link to my 'usefulness' framing, as for any sort of expected value computation you would need to take 'likelihood given context' into account.

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Jascha Achterberg @achterbrain.bsky.social · Aug 20

Now the usefulness in program generation might sometimes align with policy compression, but that depends a lot on the given time horizon one assumes for the definition of 'usefulness'.

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Jascha Achterberg @achterbrain.bsky.social · Aug 20

It also does not 100% align with my reading of it, but I found it an interesting angle. I think I find myself, naturally, being influenced by Alan Newell's take on it (which is the one John Duncan tends to reference), which is aimed at usefulness in program generation.

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Reposted by Jascha Achterberg

Julian Togelius @togelius.bsky.social · Aug 18

Can large language models play simple arcade games? Kind of. Sometimes. Slowly, and not as well as a simple search algorithm. And only if you format the input right. Of course, we made a benchmark to investigate this in more detail, because that's what we do. Paper here:
arxiv.org/html/2508.08...

GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games

arxiv.org

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Jascha Achterberg @achterbrain.bsky.social · Aug 20

Great and detailed blog post on compositionality, written by Eric Elmoznino:

ericelmoznino.github.io/blog/2025/08...

#compneuro #neuroai #neuroscience

Defining and quantifying compositional structure

What is compositionality? For those of us working in AI or cognitive neuroscience this question can appear easy at first, but becomes increasingly perplexing the more we think about it. We aren’t shor...

ericelmoznino.github.io

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Reposted by Jascha Achterberg

Steven Scholte @neurosteven.bsky.social · Aug 17

#CCN2025 is over. Over 5 days there were 6 fantastic keynotes, 550 posters, 3 community events, 3 keynote & tutorials, 3 generative adversarial collaborations, 8 Satellite events, 1 community lunch meeting, 1 cross-conference hackathon, 1 competition, coffee all day, stroopwafels on day 1,

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The Viking (Gunnar Blohm) @gunnarblohm.bsky.social · Jul 24

I'm curious if any senior nonhuman primate Neuro-AI researchers would be interested in joining Queen's University if we were to obtain a research chair position (full professor level)?

Could you please send me a confidential message to indicate your interest? [email protected]

a bald man is sitting in a chair and pointing at the camera

ALT: a bald man is sitting in a chair and pointing at the camera

media.tenor.com

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Joao Barbosa @jbarbosa.org · Aug 10

Sad to miss #CCN2025. It will be the 1st conference where a PhD working w/ me will speak 😭

go see Lubna's talk (Friday) about distributed neural correlates of flexible decision making in 🐒,

work done in collaboration w/ @scottbrincat.bsky.social @siegellab.bsky.social & @earlkmiller.bsky.social

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Reposted by Jascha Achterberg

CogCompNeuro @cogcompneuro.bsky.social · Aug 12

The registration desk for the first day is now open! If you are looking for information to get to the venue, you can find it here: 2025.ccneuro.org/venue-inform...

Look out for the CCN banners!

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Reposted by Jascha Achterberg

CogCompNeuro @cogcompneuro.bsky.social · Aug 1

Less than 2 weeks until CCN 2025 in Amsterdam! Here's everything you need to know to prepare for the 8th Cognitive Computational Neuroscience conference, August 12-15 at University of Amsterdam 🧠

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Reposted by Jascha Achterberg

Dan Goodman @neural-reckoning.org · May 29

I wrote an article earlier in the week arguing that we need to give junior researchers more independence earlier, and this should be our focus, not moonshot mega projects led by senior researchers.

I was surprised how much agreement I'm seeing.

So next question: how do we do this?

The Transmitter @thetransmitter.bsky.social · May 26

Large-scale projects run the risk of stifling scientific independence. Instead, let’s explore alternative mechanisms of collaboration, writes @neuralreckoning.bsky.social.

#neuroskyence

www.thetransmitter.org/funding/neur...

Neuroscience needs to empower early-career researchers, not fund moon shots

Large-scale projects run the risk of stifling scientific independence. Instead, let’s explore alternative mechanisms of collaboration.

www.thetransmitter.org

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Reposted by Jascha Achterberg

Dan Levenstein @dlevenstein.bsky.social · May 27

#NeuroAI finds itself facing an interesting question these days:

1) which of these now-many schemes for bio-plausible credit assignment actually operate in the brain?

-and if more than one-

2) how the hell do they operate when they, inevitably, interact?

Friedemann Zenke @fzenke.bsky.social · May 27

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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Jascha Achterberg @achterbrain.bsky.social · May 29

Super excited to share & discuss this work! This was co-lead with Valentina Mione, in collaboration with Makoto Kusunoki and Mark Buckley; supervised by John Duncan.

The link to the paper is here: www.biorxiv.org/content/10.1...

A summary is also available on my website: www.jachterberg.com/maze

Jascha Achterberg - Maze

Abstract Complex behavior calls for hierarchical representation of current state, goal, and component moves. In the human brain, a network of “multiple-demand” (MD) regions underpins cognitive control...

www.jachterberg.com

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Conclusions*
Our work reveals a distributed frontal network with specialized yet overlapping functions for flexible control. Different regions prioritize different variables while sharing information, with orthogonal coding to minimize interference, both across time and variable-related subspaces.

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Hierarchical choice code*
Temporal cross-correlation analysis revealed hierarchical coding of problem structure across all regions, with most regions being driven by temporal similarity of time windows across choices, with vlPFC also responding for the repeated order of operations across choices.

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Move codes*
Next, analysing dynamics in the "Move space", we found: Move coding develops first in vlPFC before reaching dPM; other regions showed weaker move coding. "Move space" generally orthogonal to "Goal space" (except in dmPFC).

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Goal and location codes*
We projected neural activity into the "Goal space" & measured distances between projections grouped by current position vs. goal. We saw regional specialization: vlPFC driven by location; dmPFC more driven by goal (maintained throughout trial); dPM & I/O with mixed code.

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Analysing neural subspaces*
To understand frontal cortex computations, we identified neural subspaces in relation to key variables and studied the population dynamics over the duration of the trial. For each region, we asked: Which variables drive the shape & dynamics of projections?

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Recordings*
We recorded 1374 neurons from four frontal regions with semi-chronic microelectrode arrays: ventrolateral, dorsal premotor, dorsomedial, insula/orbitofrontal. This wide coverage across frontal cortex in the same complex task makes this a unique dataset in monkey electrophysiology.

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Jascha Achterberg @achterbrain.bsky.social · May 29

*Routes through the maze*
Depending on available choice options, goal locations can be reached via 2-step or 4-step routes.

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