Rainer Engelken - Using Dynamical Systems Theory to Improve Surrogate Gradient Learning in SNNs

Anna Levina - Timescales: flexible in biological neural systems, useful for computations

Veronika Koren - Efficient encoding/transmission/transformation sensory features in a multilayer SNN

Ulaş İbrahim Ayyılmaz - Exc/inh neurons distinct roles for learning, temporal scaling, and WM

Rainer Engelken - Using Dynamical Systems Theory to Improve Surrogate Gradient Learning in SNNs

Filippo Moro - On the role of temporal hierarchy in Spiking Neural Networks

David Kappel - Spiking networks for efficient machine learning

Flash talks from SNUFA 2024

William Podlaski - Storing overlapping associative memories on latent manifolds in low-rank SNNs

Christian Machens - Computing with spikes - a geometric approach to spiking neural networks

Matteo Saponati - Feedback control algorithm for online learning in SNN and neuromorphic devices

Karim Habashy - Adapting to time: why nature chose to evolve a diverse set of neurons

Chiara Bartolozzi - Neuromorphic technologies for robotic applications, from sensing to control