Marcel S. Kehl

One week left to apply!

A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe https://www.biorxiv.org/content/10.64898/2026.02.12.705512v1

Finally out in eLife!!
"Early foveal cortex predicts the features of saccade targets through feedback from higher cortical areas."
elifesciences.org/articles/107...

Cognitive Neuroscience Research Laboratory Manager at University of Oxford Check out jobs.ac.uk for opportunities in professional services, including Cognitive Neuroscience Research Laboratory Manager. Apply today and learn more about the role.

Last week to apply! Cognitive Neuroscience Research Laboratory Manager at @oxexppsy.bsky.social (with links to @oxcin.bsky.social and @ox.ac.uk)

www.jobs.ac.uk/job/DPZ833/c...

How are memories consolidated during sleep?
Excited to share another preprint: hippocampal SWRs route memory content to the cortex via interregional co-reactivation of concept cells, optimized by slow-oscillation–spindle coupling. With the great @tschreiner.bsky.social @humansingleneuron.bsky.social

Distinct neuronal populations in the human brain combine content and context - Nature Single-neuron recordings in humans reveal largely separate content and context neurons whose coordinated activity flexibly places memory items in context.

Nature research paper: Distinct neuronal populations in the human brain combine content and context

go.nature.com/4jCecge

What is a 🧠 ripple? See our @cellpress.bsky.social perspective on ripple diversity and the grammar of memory replay: connecting key pieces from current work across the field. 👉🏼 www.sciencedirect.com:5037/science/arti...

I'm happy to share my debut as first-author with the recent publication of our article in #JNeurosci:

www.jneurosci.org/content/earl...

Big thanks again to @tschreiner.bsky.social and the whole team who made this possible! 🧠🌬️

Well done Pin-Chun! Thanks also to @oxexppsy.bsky.social,
@oxcin.bsky.social, and @erc.europa.eu

How is the semantic content of a movie processed by our brains? 🧠🎬
We decode characters, events, & other features and investigate how populations and single neurons represent movie features. Check my poster @sfn.org #sfn25!
Sat. Nov 15, 6:45-8:45pm, Hall E, poster S5
Wed. Nov 19, 9-10am, poster NN5

Structure in noise: Recurrent connectivity shapes neural variability to balance perceptual and cognitive demands in the human brain Does neural variability reflect random noise or a feature that benefits adaptive behavior? Using intracranial recordings in humans, Terlau et al. demonstrate that neural variability results from the r...

New work from the lab published in @cp-neuron.bsky.social by @jonasterlau.bsky.social and Jan Martini. We describe that trial-by-trial variability indexes recurrent connectivity across the cortical hierarchy, which supports reliable and flexible coding www.cell.com/neuron/abstr... (1/4)

My Lab @unlv.edu is recruiting motivated students interested in human memory and brain research! Learn #EEG, #fMRI, and data analysis while exploring how we remember 🧠
📧 DM me or check out #PhD program www.unlv.edu/degree/phd-n... & www.unlv.edu/psychology/g...
Plus, Vegas is a fun place to live!🤟

View from your office onto Giessen and surrounding villages.

Please repost! I am looking for a PhD candidate in the area of Computational Cognitive Neuroscience to start in early 2026.

The position is funded as part of the Excellence Cluster "The Adaptive Mind" at @jlugiessen.bsky.social.

Please apply here until Nov 25:
www.uni-giessen.de/de/ueber-uns...

8/8: A huge thanks to Katharina Karkowski and everyone contributing! @humansingleneuron.bsky.social, Florian Mormann, Yair Lakretz, @martinhebart.bsky.social, @standehaene.bsky.social, @alanadarcher.bsky.social @treber.bsky.social, @yjqin.bsky.social, Philipp Karkowski, Valeri Borger & Rainer Surges

7/8: Which semantic models best explain these neuronal responses?
Continuous, vector-based embeddings capture MTL activity more accurately than categorical or network-based models, shedding light on how meaning is structured in the human brain.

6/8: Investigating graded responses across neurons revealed region-specific tuning of MTL subregions. Amygdala neurons, for example, showed a preference for food items. These tuning patterns link single-neuron coding with large-scale activity seen in human brain imaging.

5/8: This example amygdala neuron responds to several food- and drink-related items but not to unrelated items (such as the bus).

4/8: Human MTL neurons exhibit semantic tuning, with graded responses that follow narrow and steep tuning curves (steepest in hippocampus).
Neurons preferentially respond to semantically related stimuli, and their firing rates correlate with semantic similarity to preferred stimuli.

3/8: Neuronal tuning curves are core coding principles in neuroscience (e.g., in vision and audition). They can vary in width and steepness. Using semantic embeddings and standardised image sets (THINGS), we presented items at controlled semantic distances to map the local semantic vicinity.

2/8: The discovery of concept cells sparked debate about binary vs distributed coding. Here, we developed a closed-loop algorithm that adaptively presents images semantically related to response-eliciting stimuli to explore the semantic tuning of MTL neurons.

Semantic Tuning of Single Neurons in the Human Medial Temporal Lobe The Medial Temporal Lobe (MTL) is key to human cognition, supporting memory, emotional processing, navigation, and semantic coding. Rare direct human MTL recordings revealed concept cells, which were ...

🚨Preprint: Semantic Tuning of Single Neurons in the Human Medial Temporal Lobe

1/8: How do human neurons encode meaning?
In this work, led by Katharina Karkowski, we recorded hundreds of human MTL neurons to study semantic coding in the human brain:

doi.org/10.1101/2025...

Semantic Tuning of Single Neurons in the Human Medial Temporal Lobe https://www.biorxiv.org/content/10.1101/2025.10.21.682935v1

Hippocampal stimulation reveals causal role of persistent neural activity in human working memory https://www.biorxiv.org/content/10.1101/2025.08.20.671301v1

Neuronal theta-phase locking increased during periods of elevated theta power, when aperiodic activity exhibited steeper slopes, and when clear theta oscillations were detected. Theta-phase locking was similarly strong during the successful and unsuccessful encoding and retrieval of memories. Some neurons changed their preferred theta phases between encoding and retrieval.

Excited to share our new paper on theta-phase locking of single neurons during human spatial memory:

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

With @lukaskunz.bsky.social, Joshua Jacobs, and our colleagues from the University of Freiburg

Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm Optically pumped magnetometers (OPMs) offer a promising advancement in noninvasive neuroimaging via magnetoencephalography (MEG), but establishing their reliability and comparability to existing metho...

How reliable is OPM-MEG, and how does it compare to other neuroimaging modalities? 🤔

In a new preprint with ‪@s-michelmann.bsky.social‬, we evaluate the reliability of OPM-MEG within & between individuals, and compare it to fMRI & iEEG during repeated movie viewing. 🧠

📄 doi.org/10.1101/2025...

Unattended working memory items are coded by persistent activity in human medial temporal lobe neurons - Nature Human Behaviour Paluch et al. show that unattended working memory items, as well as attended ones, are encoded in persistent activity in the medial temporal lobe.

In this Article, Paluch et al. show that unattended working memory items, as well as attended ones, are encoded in persistent activity in the medial temporal lobe. @jankaminski.bsky.social
www.nature.com/articles/s41...

Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results.

www.nature.com/articles/s42...

How does our brain learn that thunder follows lightning? We don't just remember two separate events; we build a predictive model to anticipate the world. My research dives into this very question: how we learn and predict the order of events. 🧵👇 1/9 #neuroscience #memory #sleep

How does sequence learning reshape item representations? We found that the brain incorporates high-level successor representations even outside the sequence task. This process is supported by slow-wave sleep. Great work by @xianhuihe.bsky.social and the team!

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

Sleep strengthens successor representations of learned sequences https://www.biorxiv.org/content/10.1101/2025.06.11.658893v1