2nd Best Poster Price: Enrico Di Iuri

Best Presentation: Marina Vannucci

Interview with Prof. Dr.phil.nat. Walter Senn (in german)

Interview with Mihai Petrovici (in german)

Dr. Anna Obenauf, IMP Vienna Winner of the Dr. Josef Steiner Award 2025

Best Poster Flash Talk: Thomas Forro

Best Oral Presentation: Masaba Sayor Baroi

A DFG ISF project was awarded to an international consortium including M. Petrovici's group.

This award recognizes exceptional scientific work, innovation, and contribution to the life sciences.
Lucilla Giammarino did her PhD at the Institute of Physiology, under the supervision of Prof. Katja Odening.

Interview with Dr. Federico Benitez and Prof. Dr.phil.nat. Walter Senn (in german)

Uncertainty is omnipresent. While humans and other animals take uncertainty into account during decision making, it remains unclear how it is represented in cortex. Recent theoretical work on uncertainty computation in cortical neurons predicts a stimulus-triggered decrease of the within-trial membrane potential variability. We leverage simulation-based inference to gain insights about the membrane potential statistics underlying single unit spiking activity.

How physical neuronal networks, bound by spatio-temporal locality constraints, can perform efficient credit assignment, remains an intriguing question. Both backward- and forward-propagation algorithms rely on assumptions that violate this locality in various ways. We introduce Generalized Latent Equilibrium (GLE), a framework for fully local spatio-temporal credit assignment in physical, dynamical neuronal networks.

Cardiac ryanodine receptors (RyR2) are critical for heart contraction. Here, we use 3D MINFLUX microscopy to image receptor subunits and RyR2 orientation with nanometre resolution, thereby providing a molecular view of the organisation and clustering of these cardiac receptors.

MINFLUX is a powerful single-molecule approach capable of achieving high spatially isotropic resolution in three dimensions. Current implementations collect localizations strictly serially, but criteria for when to terminate acquisition are often unclear. Here we measure labelling efficiency and provide guidelines how to maximise marker detection and acquire high quality molecular resolution data of biological samples.

Owing to their significant advantages in terms of bandwidth, power efficiency, and latency, optical neuromorphic systems have arisen as interesting alternatives to digital electronic devices. Recently, photonic crystal nanolasers with excitable behavior were first demonstrated. In this work, we provide a detailed derivation of translation rules from conventional sampling networks, such as Boltzmann machines, to photonic spiking networks and demonstrate their functionality across a range of generative tasks.