Dear friends and colleagues, as part of the Learning in Spiking Neural Networks Seminar Series are we cordially inviting you to the following presentation. Speaker: Felix Effenberger - Ernst Strüngmann Institute, Frankfurt am Main, Germany Title: A biology-inspired recurrent oscillator network for computations in high-dimensional state space Abstract: Biological neuronal networks have the propensity to oscillate. However, it is unclear whether these oscillations are a mere byproduct of neuronal interactions or serve computational purposes. Therefore, we implemented hallmark features of the cerebral cortex in recurrent neuronal networks (RNNs) simulated in silico and examined their performance on common pattern recognition tasks after training with a gradient-based learning rule. We find that by configuring network nodes as damped harmonic oscillators (DHOs), performance is substantially improved over non-oscillating architectures and that the introduction of heterogeneous nodes, conduction delays, and network modularity further improved performance. We furthermore provide a proof of concept of how unsupervised Hebbian learning can work in such networks. Analyses of network activity illustrate how the nonlinear dynamics of coupled DHOs drive performance, and provide plausible a posteriori explanations for a number of physiological phenomena whose function so far has been elusive. Also check out the corresponding preprint [1]. Time: 3rd of March 2023 at 14:00 Central European Time. Venue: Hybrid meeting, both on Zoom [2] and in person at the Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Lecture Hall 100. Best wishes in the name of the GRADE Initiative Learning in Spiking Neural Networks, Tristan Stöber Ps: Videos of previous events can be found on our youtube channel [3] [1] https://www.biorxiv.org/content/10.1101/2022.11.29.518360v1.abstract [2] https://ruhr-uni-bochum.zoom.us/my/tristanstoeber?pwd=YnZ2RlZVTUZPU2ZhNlFyRF... [3] https://www.youtube.com/@lisnn.channel5458