Jens-Bastian Eppler

@j-b-eppler.bsky.social

Postdoc in Computational Neuroscience | CRM Barcelona Mostly interested in the mechanisms underlying learning, forgetting, memory formation, and most recently also creativity. And "representational drift". https://jb-eppler.github.io/

This is not what I usually work on, but sometimes you get bored... 😜 But what actually IS boredom? My collaborator Johannes just put out this beautiful new preprint tackling exactly that question, from behaviour all the way to neural population codes. doi.org/10.64898/202... 1/5 🧠🧪

Boredom and the representation of information content in the neocortex

Boredom – a pervasive mental state – promotes the pursuit of novel information by assigning negative value to monotonous conditions. Yet, how the brain extracts and represents the information content of ongoing sensory experience remains poorly understood. Here, we combine behavioral assays, neurophysiological recordings and computational modeling across humans and mice to investigate how sensory information shapes boredom-related behavior. In a cross-species choice task, both humans and mice robustly avoid monotonous sources of sensory stimulation. We formalize perceived monotony using empirical entropy as a measure of information content and show that monotony avoidance scales directly with low entropy and in humans correlates with boredom experience. Human electroencephalography and mesoscopic calcium imaging in mice reveal that the recruitment of neocortical activity tracks stimulus entropy. Two-photon calcium imaging in the auditory cortex of mice further uncovers a stimulus-invariant population code for entropy, supported by neurons tuned to information content. A recurrent network model reproduced this code through an interplay of afferent depression and recurrent facilitation. Together, we demonstrate how the information content of sensory experience is represented in cortical population activity, providing a basis for boredom-related avoidance behavior. Thus, our findings link synaptic and neuronal dynamics to boredom, acting as a safeguard mechanism to ensure high information input to the brain. ### Competing Interest Statement The authors have declared no competing interest. This work was supported by research grant Deutsche Forschungsgemeinschaft CRC1080-C05 (S.R.), Deutsche Forschungsgemeinschaft SPP 2041 Project #347573108 (S.R.), Deutsche Forschungsgemeinschaft/Agence nationale de la recherche Project #431393205 (S.R.), Deutsche Forschungsgemeinschaft DIP M-BM-^SNeurobiology of ForgettingM-BM-^T (S.R.), Rhine-Main University Alliance RMU (S.R.), JST Moonshot R & D #JPMJMS2292-A1-02 (O.T.), Deutsche Forschungsgemeinschaft #512007073 SFB/TRR379 TP B02 (O.T.), BMFTR German Center for Mental Health (DZPG, grant 01EE2505C & VISIONS TRESPE 01EE2507Q) (O.T), and a fellowship of the Focus Translational Neuroscience Mainz (J.S., L.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

doi.org

As promised (and only slightly late 😅): here's a figure-by-figure walk-through of our preprint "Random network structure stabilizes neural manifolds". This one is a bit more for the nerds. For an overview version 👉 bsky.app/profile/j-b-... Preprint: doi.org/10.64898/202... 1/8 🧠🧪

Random network structure stabilizes neural manifolds

Neuronal activity patterns change continuously over days and weeks, a phenomenon known as representational drift. Despite this, the geometric structure of population representations, namely the pairwise similarities between stimulus-evoked activity patterns, remains remarkably stable. How can ongoing changes in activity be consistent with stable representational similarity? We show that this is a generic consequence of random connectivity: in networks with random connectivity, output similarity is a monotonically increasing function of input similarity, independent of the specific connectivity pattern. Drift, whether driven by random synaptic turnover or Hebbian plasticity, merely transitions the network between random instantiations, leaving similarity intact. This extends to recurrent architectures and to deep neural networks, where continued training beyond performance saturation produces activity drift while preserving representational similarity. Although connectivity in the brain is not random, networks trained on high-dimensional inputs acquire connectivity that behaves statistically like a random projection, making these results broadly applicable to biological neural circuits. ### Competing Interest Statement The authors have declared no competing interest. Spanish Ministry of Science and Innovation, PCI2023-145967-2, PID2021-124702OB-I00 Spanish State Research Agency (AEI) – Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D, CEX2020-001084-M German Research Foundation (DFG), FOR 5368 ARENA

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Jens-Bastian Eppler@j-b-eppler.bsky.social · 2mo ago

New preprint: Random network structure stabilizes neural manifolds We’re excited to share our new work on representational drift. doi.org/10.64898/202... Representational drift poses a puzzle. 👇 A short thread below. In the next days a figure by figure walk through will follow. 1/5 🧪🧠

Congrats to my office mate @d-ferro.bsky.social on his fantastic new paper, now out in @natcomms.nature.com! The gist: as the jackpot gets "closer", macaques' decisions become more accurate, but also riskier. Even cooler, all this is mirrored in neural activity. Elegant study and super clear. 🧠🧪

Demetrio Ferro@d-ferro.bsky.social · last wk.

Excited to share our latest publication, out now in @natcomms.nature.com !🧠🔍 Accumulating virtual rewards enhances both decision-making accuracy and neural value encoding in dorsal anterior cingulate cortex by dynamically shifting a goal-dependent internal reference point. doi.org/10.1038/s414...

Very cool work from former colleagues: doi.org/10.48550/arX... They render 3D objects into 2D images, systematically varying parameters like hue, lighting, camera angle, etc. Having tightly controlled stimulus sets like this will help compare representations across AI and biological networks. 🧪🧠

MAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space

Modern vision models achieve strong performance on standard benchmarks, yet their aggregate accuracy reveals little about which scene properties drive their predictions. Existing robustness benchmarks...

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This will be fun. Including my talk on how neuronal activity manifolds are preserved in random networks on Tuesday. Hope to see some of you there!

Hospital del Mar Research Institute@researchmar.bsky.social · 3mo ago

🧠11-12/5: #GreenHippocampus will bring together international experts in hippocampal physiology. 🔬Organised by Manuel Valero @researchmar.bsky.social + @lmprida.bsky.social Daniel Bendor & Lisa Roux. 👥150 places available: www.researchmar.net/agenda/1078/ @cnrs.fr @csic.es @ucl.ac.uk @prbb.org

Wanna do neuroscience in Paris but can't find interesting lab? Want to come do a sabbatical but don't know who to collaborate? Check this webpage aggregating ~all the neuroscience labs (+200) in Paris. ⚠️only the information of 'verified' profiles is reliable⚠️ Please retweet 🙏 parisneuro.fr

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And now for something completely different... New paper out: Towards circuit mechanisms of the creative process doi.org/10.1080/1040... How does the brain generate creative ideas? We argue that creativity can be understood at the level of neural circuits - not just behavior or cognition. 🧪🧠 1/4

Towards Circuit Mechanisms of the Creative Process

Creativity stands as one of the most intriguing aspects of cognition, attracting cross-disciplinary investigation due to its multifaceted nature and profound implications. While significant progres...

doi.org

As promised: a detailed figure-by-figure thread on our @pnas.org paper: doi.org/10.1073/pnas... We use signal correlations and noise correlations in chronic imaging data to show that representational drift is shaped by a balance between Hebbian and stochastic changes. Let’s dive in 👇 🧠🧪 1/9

Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning | PNAS

Recent evidence indicates that even under stable environmental and behavioral conditions, responses to sensory stimuli undergo continuous reformatt...

doi.org

Jens-Bastian Eppler@j-b-eppler.bsky.social · 6mo ago

New @pnas.org paper out 🎉 “Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning” 👉 doi.org/10.1073/pnas... What drives representational drift in neural populations? Here’s the short version. 👇 🧪🧠 1/5

New @pnas.org paper out 🎉 “Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning” 👉 doi.org/10.1073/pnas... What drives representational drift in neural populations? Here’s the short version. 👇 🧪🧠 1/5

PNAS

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

doi.org

Loved working with our amazing outreach team on this short video about representational drift! @crmatematica.bsky.social 🧠🧪 In it, I explain the points we make in our recent review in CONEUR: doi.org/10.1016/j.co...

Centre de Recerca Matemàtica | CRM-CERCA@crmatematica.bsky.social · 10mo ago

Memories feel fixed, but the brain never stands still. @j-b-eppler.bsky.social talks about representational drift; how memories remain stable even as the neurons behind them constantly change. youtu.be/z63fmYSBcB0 #Neuroscience #Mathematics #Memory #Brain #CognitiveScience #Neurobiology #Research

My talk at the WWTNS is now online! In it, I explore how both random processes and Hebbian learning shape representational drift: www.youtube.com/watch?v=WH4P... In the end I raise the question: How can neuronal activities change while representational similarity is preserved? 🧠🧪

Representational drift reflects ongoing balancing of stochastic changes by.... | Jens-Bastian Eppler

YouTube video by The Theoretical Neuroscience Channel

youtube.com

Great new paper by my colleagues Gloria & Alex, and @lichengzou.bsky.social. Hebbian plasticity accounts for much of representational drift: Drift is ongoing memory storage, not noise. Also reconciles contradictory findings on stability after repeated exposure. www.nature.com/articles/s41... 🧠🧪

Representational drift as the consequence of ongoing memory storage - Scientific Reports

Scientific Reports - Representational drift as the consequence of ongoing memory storage

nature.com

An alle Forscher*innen, die KI verwenden/erforschen und deutsch sprechen: Macht mit! Ich war jetzt schon paarmal dabei und es hat immer sehr viel Spaß gemacht. (Dieses Mal habe ich leider keine Zeit...) 🧪

Wissenschaft im Dialog@w-i-d.de · last yr.

Ab sofort könnt ihr euch für die neue Runde von I’m a Scientist rund um #KI bewerben. Tauscht euch mit Schüler*innen zu eurer Forschung und aktuellen Fragen zu KI aus. Die Live-Chats finden vom 22. September bis zum 1. Oktober statt. Bewerbungsschluss: 31. August: imascientist.de/bewerbungsfo...

 "Forscher*innen für neue Themenrunde gesucht". Logo I'm a Scientist, "KI", "Bis zum 31. August bewerben”, Logo Wissenschaft im Dialog, RHET AI, VolkswagenStiftung, Forschungsbörse

Habe beim deutschen #ImAScientist zum Thema KI mitgemacht. Dort können Schüler*innen mit Wissenschaftler*innen chatten. Ist oft super chaotisch mit einer ganzen Klasse zu chatten, aber macht richtig Spaß. Kann ich wirklich nur empfehlen! Meine Eindrücke dazu: imascientist.de/2025/07/09/d... 💬🧪🧠👩‍🔬👨‍🔬

„Der Austausch mit den Schüler*innen hilft dabei, den Spaß und die Freude am Forschen nicht aus den Augen zu verlieren.” – Jens-Bastian Eppler, Gewinner der Themenrunde Künstliche Intelligenz im Inter...

imascientist.de

Just submitted my abstract. :) You still have the whole weekend to submit yours for a contributed talk at the #BernsteinConference 2025! Probably my favorite conference, and once again the speaker lineup looks fantastic. Hope to see many of you in Frankfurt.

Bernstein Network Computational Neuroscience@bernsteinneuro.bsky.social · last yr.

⌛ One week left to submit your contributed talk abstracts for the #BernsteinConference 2025! 🗓️ Deadline: June 17, 15:00 CEST All info and submission 👉 bernstein-network.de/bernstein-co...

New paper (I played a minor role in)! Representational similarities in mouse auditory cortex remain stable during drift. When Taka Noda ablated neurons, these similarities destabilized but recovered within days - previously unresponsive neurons stepped up to fill gaps. doi.org/10.1038/s415... 🧪🧠

Homeostasis of a representational map in the neocortex - Nature Neuroscience

This study investigates how homeostatic mechanisms endow sensory representations in the auditory cortex with resilience against neuron loss. The map of sounds has the ability to recover after microabl...

doi.org