Friedemann Zenke

Congrats to Fabian Mikulash, a postdoc in the @fzenke.bsky.social lab, for being awarded a Marie Skłodowska-Curie Actions fellowship! His project aims to develop a new theory—tested with real brain data—explaining how neurons decide when to trust what we see versus what we expect 🧠

Our paper is out in @natneuro.nature.com!

www.nature.com/articles/s41...

We develop a geometric theory of how neural populations support generalization across many tasks.

@zuckermanbrain.bsky.social
@flatironinstitute.org
@kempnerinstitute.bsky.social

1/14

Our work with @georgkeller.bsky.social on testing predictive processing (PP) models in cortex is out on biorvix now! www.biorxiv.org/content/10.6... A short thread on our findings and thoughts on where we should move on from PP below.

The hippocampal map has its own attentional control signal!
Our new study reveals that theta #sweeps can be instantly biased towards behaviourally relevant locations. See 📹 in post 4/6 and preprint here 👉
www.biorxiv.org/content/10.6...
🧵(1/6)

With some trepidation, I'm putting this out into the world:
gershmanlab.com/textbook.html
It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class.

My hope is that this will be a living document, continuously improved as I get feedback.

Careers Careers on Simons Foundation

Joint junior faculty position in Computational Neuroscience, between Ctr for Computational Neuroscience at @flatironinstitute.org and the CUNY Graduate Center @thegraduatecenter.bsky.social . Application deadline: 16 Jan 2026!

www.simonsfoundation.org/flatiron/car...
cuny.jobs/new-york-ny/...

Thanks, Rich!

Thanks so much.

Thank you!

I’m very grateful to the FMI, the tenure committee, inspiring colleagues, and all the hidden supporters who made this possible. Huge thanks to past and present group members for their curiosity and creativity. Excited for the next chapter.

Three types of remapping with linear decoders: A population-geometric perspective Author summary Place cells of the hippocampus form unique activity patterns in different environments, a process called remapping. However, it is not clear what the relationship is between changes in ...

I’m happy to share some recent work out in PLOS Computational Biology with @guille-martin.bsky.social and Christian Machens at @champalimaudr.bsky.social . We use neural coding and population geometry to study different perspectives on hippocampal remapping.

journals.plos.org/ploscompbiol...

Lindsay Lab - Postdoc Position Artificial neural networks applied to psychology, neuroscience, and climate change

Spread the word: I'm looking to hire a postdoc to explore the concept of attention (as studied in psych/neuro, not the transformer mechanism) in large Vision-Language Models. More details here: lindsay-lab.github.io/2025/12/08/p...
#MLSky #neurojobs #compneuro

Finally got the job ad—looking for 2 PhD students to start spring next year:

www.gao-unit.com/join-us/

If comp neuro, ML, and AI4Neuro is your thing, or you just nerd out over brain recordings, apply!

I'm at neurips. DM me here / on the conference app or email if you want to meet 🏖️🌮

Come work with us!!!

Joint modelling of brain and behaviour dynamics with artificial intelligence - Nature Reviews Neuroscience Artificial intelligence is rapidly advancing our mechanistic understanding of the shared structure between the brain and higher-order behaviours. In this Review, Mathis and Mathis synthesize state-of-...

Joint modelling of brain and behaviour dynamics with artificial intelligence

www.nature.com/articles/s41...

Thanks! There is a notable difference, though: in Nejad et al. (2025), L5 is trained with a reconstruction loss, i.e., an autoencoder (see Eqs. 4–6 from the methods below). L2/3 then predicts the autoencoder's latent state via a supervised next-step loss. That shouldn't be conflated with a JEPA.

Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex Scenes are complex, yet structured collections of parts, including objects and surfaces, that exhibit spatial and semantic relations to one another. An effective visual system therefore needs unified ...

🚨New Preprint!
How can we model natural scene representations in visual cortex? A solution is in active vision: predict the features of the next glimpse! arxiv.org/abs/2511.12715

+ @adriendoerig.bsky.social , @alexanderkroner.bsky.social , @carmenamme.bsky.social , @timkietzmann.bsky.social
🧵 1/14

6/ Finally, we build a hierarchical JEPA version of our model and outline how its architecture could map onto cortical microcircuits, toward a predictive-processing framework with mechanistic links to neuroanatomy. Read the full story here 👇
🔗 doi.org/10.1101/2025...

5/ Importantly, RPL captures representational motifs across multiple species and cortical areas: On the one hand, successor-like structures resembling those in human V1. On the other hand, its abstract sequence representations are comparable to macaque PFC.

4/ From raw video streams and without supervision, RPL learns: invariant object identity, equivariant motion variables (position, velocity, orientation, etc.), and a world model that allows simulating plausible motion trajectories entirely in latent space.

3/ Recent studies indicate that, aside from plausibility, representation-space predictive models like JEPAs also learn more abstract representations than input-space generative models, which tend to focus on low-level details (cf @yann-lecun.bsky.social)

2/ RPL operates entirely in latent space, avoiding the anatomical issues of predictive coding models that compute prediction errors in input space. Instead, the network predicts future internal representations through a specific recurrent circuit structure.

1/6 New preprint 🚀 How does the cortex learn to represent things and how they move without reconstructing sensory stimuli? We developed a circuit-centric recurrent predictive learning (RPL) model based on JEPAs.
🔗 doi.org/10.1101/2025...
Led by @atenagm.bsky.social @mshalvagal.bsky.social

Excited to see the paper fully published. It's an important milestone for training SNNs with exact gradients, replacing our earlier tricks of a "delay line augmentation" to capture temporal relationships. Delays can now be learnt alongside weights naturally. Amazing work @mbalazs98.bsky.social !

Exploiting heterogeneous delays for efficient computation in low-bit neural networks Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is the delay: the brain...

Psst - neuromorphic folks. Did you know that you can solve the SHD dataset with 90% accuracy using only 22 kb of parameter memory by quantising weights and delays? Check out our preprint with @pengfei-sun.bsky.social and @danakarca.bsky.social, or read the TLDR below. 👇🤖🧠🧪 arxiv.org/abs/2510.27434

Spiking NN fans - the #SNUFA workshop (Nov 5-6) agenda is finalised and online now. Make sure to register (free) soon. (Note you can register for either day and come to both.)

Agenda: snufa.net/2025/
Registration: www.eventbrite.co.uk/e/snufa-2025...

Thanks to all who voted on abstracts!

🤖🧠🧪

Message for participants of the #SNUFA 2025 spiking neural network workshop. We got almost 60 awesome abstract submissions, and we'd now like your help to select which ones should be offered talks. Follow the "abstract voting" link at snufa.net/2025/ to take part. It should take <15m. Thanks! ❤️

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.

I’m super excited to finally put my recent work with @behrenstimb.bsky.social on bioRxiv, where we develop a new mechanistic theory of how PFC structures adaptive behaviour using attractor dynamics in space and time!

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

Truly honored (and a little overwhelmed) to see our work featured in The Transmitter's "This Paper Changed My Life." Huge thanks to @neural-reckoning.org for the kind words - and to our amazing community that keeps pushing spiking neural network research forward 🙏