Understanding how the brain constructs and simulates world models is a fundamental challenge in neuroscience. We are looking for someone to join our crew to explore the neuronal circuit mechanisms for learning and simulating worldContinue reading
Discussing science at les Rasses
Julia gave a nice talk on spiking nets at a focus meeting at Les Rasses with the obligatory walk up the Chasseron. Thanks to Christian Lüscher and Andreas Lüthi for organizing the meeting. Photo byContinue reading
Kavli Neuroscience Workshop
Julian and Friedemann very much enjoyed the Kavli Neuroscience extended workshop “Theories of neural computations in the era of large-scale recordings” in Trondheim. Thanks again to all the organizers and participants for a truly stimulatingContinue reading
Hiring: Deep learning-based design of synthetic regulatory elements for cell-type-specific targeting
Understanding the molecular mechanisms that drive cell-type-specific gene expression in the brain is crucial for advancing circuit neuroscience. The wealth of genome-wide information on gene regulation available today puts us in the position to decipherContinue reading
High school students build neuronal circuits
At the annual Tage der Genforschung event, the FMI opens its doors to interested high school students from the neighboring cities. This year team members from our group showed the students how to build simpleContinue reading
Preprint: Theories of synaptic memory consolidation and intelligent plasticity for continual learning
We are happy to present our new preprint:Zenke, F., Laborieux, A., 2024. Theories of synaptic memory consolidation and intelligent plasticity for continual learning. https://arxiv.org/abs/2405.16922 It is a book chapter in the making which covers theContinue reading
Theoretical Neuroscience Podcast
In the 11th episode of the Theoretical Neuroscience Podcast Gaute and Friedemann discuss about end-to-end learning with spiking neurons and self-supervised learning.
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks
Surrogate gradients (SGs) are empirically successful at training spiking neural networks (SNNs). But why do they work so well, and what is their theoretical basis? In our new preprint led by Julia, we answer theseContinue reading
