CompNeuro Lab

Computational Neuroscience research group led by Walter Senn @unibern

[bioRxiv @biorxivpreprint]

#PeerReview from @eLife of 👉🏿 Uncertainty-modulated prediction errors in cortical microcircuits https://t.co/cJV5ozy3gm #biorxiv #reviewedpreprint

[Marc Züst @MarcZuest]

New #paper alert! Here, we show that #auditory #stimulation can be used to selectively #suppress slow wave #sleep by 30% without affecting total sleep time.
https://t.co/CVUECFQn9F

[bioRxivGPT @bioRxivGPT]

🏷️:Unsigned temporal difference errors in cortical L5 dendrites during learning

🔗:https://t.co/iU91NXDZmx

[Quantitative Biology (Neurons and Cognition) @BioPapers]

Prospective and retrospective coding in cortical neurons. https://t.co/T2T9JlN2OD

[Emerson F. Harkin @efharkin_]

What’s the point of strong spike frequency adaptation in serotonin neurons?

This new work offers a clue: adaptation lets neurons broadcast a prediction of their future output, preventing slow downstream regions from lagging behind. 💡

https://t.co/4ZnEIfJGHN

[Quantitative Biology (Neurons and Cognition) @BioPapers]

Prospective and retrospective coding in cortical neurons. https://t.co/T2T9JlN2OD

[bioRxiv Neuroscience @biorxiv_neursci]

Increased perceptual reliability reduces membrane potential variability in cortical neurons https://t.co/vEhwsTrBC1 #biorxiv_neursci

[Mackenzie Weygandt Mathis, PhD @TrackingActions]

@LecoqJerome You’ll need to add this one to the graph! Beautiful work from Walter Senn and colleagues: https://t.co/rggozbSoqU

[cackerman21 @cackerman21]

DreamOn: a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers

https://t.co/CFKbfj2oum

[fellow ⚚ traveler ❤️‍🔥 @architectonyx]

@fchollet a cool recent paper on cortical circuit function is this one, Bengio was actually a co-author on it

has cool ties with predictive coding, biologically plausible/backprop free learning

https://t.co/ZDdwCMYm5w

[NeuroTMA Lab @NeuroTMA]

For more details see our repo https://t.co/aAi0juviVU and the preprint our researchers from @unibern and @BrainScaleS published https://t.co/bJ8ChFwVAb

[NeuroTMA Lab @NeuroTMA]

Happy to see that our open source palm-sized analogue neuromorphic hardware Lu.i are used to explain key neuromorphic engineering concepts!
https://t.co/RrKGKZwQow

[Natalie Schaworonkow @nschawor]

coolest conference souvenir: electronic neuron circuit lu.i

on this board: an implementation of a leaky integrate and fire neuron, with the membrane voltage visualized with LEDs (or connect an oscilloscope to see the traces).

[OCNS @CNSorg]

📣 Elections for the new OCNS Directors are now open. All current OCNS members are eligible to vote.

Candidates info page: https://t.co/Vo8DaYZD1g

Voting page: https://t.co/gKmOuzl4NG

Election procedures: https://t.co/lqqtF958sp

Hurry up. The deadline is tomorrow 📅

[NeuroTMA Lab @NeuroTMA]

Happy to present today and tomorrow three of our projects at #SNUFA24 Go Team!

[Universität Bern @unibern]

🧠 Forschende der #unibern haben ein Modell entwickelt, das zeigt, wie KI-Systeme Bewusstsein erlangen könnten. Sie schlagen den „Human-AI Deal“ vor, um den Umgang zwischen Mensch und Maschine zu regeln: https://t.co/0ItLSyam5z.
@MedFacultyUniBE

[NeuroTMA Lab @NeuroTMA]

Happy to have participated and presented our work at the last @BernsteinNeuro!

[CompNeuro Lab @CompNeuro_WS]

Pleased to present our latest paper with Cyriel Pennartz in AI & Ethics, thanks to our fruitful collaboration made possible by @HumanBrainProj
"The conductor model of consciousness, our neuromorphic twins, and the human-AI deal" https://t.co/D9ljnwRTVX &👇@unibern Press Release

[Universität Bern @unibern]

🧠 Researchers at #unibern have developed a model suggesting that #AI systems could gain consciousness. They propose a "Human-AI Deal" to regulate the human-machine relationship: https://t.co/UkmAN3ESdb.
@MedFacultyUniBE

[NeuroTMA Lab @NeuroTMA]

And two posters:
"DelGrad: Exact gradients in spiking networks for learning transmission delays and weights" (P. Lake)

"Emergence of Synfire Chains in Functional Multi-Layer Spiking Neural Networks" (J. Göltz)

@unibern @EBRAINS_eu #BernsteinConference

[bioRxiv Neuroscience @biorxiv_neursci]

Hierarchy of prediction errors shapes the learning of context-dependent sensory representations https://t.co/KA8AUFpeyd #biorxiv_neursci