Bernstein Network News. Find the latest news from our researchers regarding current research results, new research projects and initiatives as well as awards and prizes.
Direction-selective neuron subtypes detect complex motion patterns and not uniform directions of motion
EU funding for work on the adaptive properties of visual systems of insects
An international team of researchers from Tübingen and Cold Spring Harbor (New York) has found a pioneering way of determining at what pace changes typically happen. The new method avoids previous systematic errors in estimating timescales, for example of neural activity in the brain. The results are now being published in the journal Nature Computational Science; first applications of the method to neural recordings from the visual cortex highlight it as a powerful tool for neuroscience and many other disciplines.
A new artist in residence program explores the interplay between AI research and art
Researchers at the Bernstein Center in Freiburg develop a new model to understand plastic processes in the brain's networks
The Brains for Brains Young Researcher Award is directed to young scientists outside Germany who have shown their outstanding potential at a very early career stage. It is endowed with a travel grant of 2.000 € allowing a trip to the Bernstein Conference and individually planned visits to labs within the Bernstein Network Computational Neuroscience. This is one of two prizes awarded by the Bernstein Network. The aim is to spark international young researchers interest for a research career in Germany. Application deadline is April 24, 2022
Four female and six male researchers will receive the Heinz Maier-Leibnitz Prize this year, the top award for early career investigators in Germany. This was the result of a decision made by a selection committee appointed by the DFG and the Federal Ministry of Education and Research (BMBF).
The prizes are each worth €20,000 and were handed over at a virtual event on 3 May.
New AI algorithm generates innovative substances on the basis of desired properties
Whether in medicine, battery research, or materials science, researchers everywhere are seeking innovative substances. In the process, they can often predict the desired chemical and physical properties in great detail, right down to atomic level. However, the range of all potential chemical compounds alone is so vast that it would take years to find the appropriate substance. An interdisciplinary research group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at Technische Universität Berlin has now developed an algorithm which uses AI to implement inverse chemical design and thus generate targeted molecules based on their desired properties. The research group's publication titled "Inverse design of 3d molecular structures with conditional generative neural networks" has now been published in the renowned journal Nature Communications.
Researchers at the University of Freiburg develop a new method for controlled interrogation and recording of neuronal activity
When Marius Schneider starts his PhD at the Ernst Strüngmann Institute for Neuroscience, he would not dare to dream that the results of his first project would be published shortly afterwards in one of the leading journals in his field - and turn an established theory upside down ...