Dan Goodman on Nostr: The #SNUFA24 final program is out and the event is next Tue-Wed! If you love spiking ...
The #SNUFA24 final program is out and the event is next Tue-Wed! If you love spiking neural networks, click on the link below to check it out and register (free).
https://snufa.net/2024/
Day 1:
Chiara Bartolozzi, IIT Genova (invited)
Karim Habashy
Adapting to time: why nature chose to evolve a diverse set of neurons
Matteo Saponati
A feedback control algorithm for online learning in Spiking Neural Networks and Neuromorphic devices
Christian Machens, Champalimaud (invited)
William Podlaski
Storing overlapping associative memories on latent manifolds in low-rank spiking networks
Flash talks
Day 2
David Kappel, University of Bochum (invited)
Filippo Moro
On the role of temporal hierarchy in Spiking Neural Networks
Rainer Engelken
Using Dynamical Systems Theory to Improve Surrogate Gradient Learning in Spiking Neural Networks
Anna Levina, Uni Tübingen (invited)
Ulaş İbrahim Ayyılmaz
Excitatory and inhibitory neurons exhibit distinct roles for task learning, temporal scaling, and working memory in recurrent spiking neural network models of neocortex
Veronika Koren
Efficient encoding, transmission and transformation of sensory features in a multilayer spiking network
https://snufa.net/2024/
Day 1:
Chiara Bartolozzi, IIT Genova (invited)
Karim Habashy
Adapting to time: why nature chose to evolve a diverse set of neurons
Matteo Saponati
A feedback control algorithm for online learning in Spiking Neural Networks and Neuromorphic devices
Christian Machens, Champalimaud (invited)
William Podlaski
Storing overlapping associative memories on latent manifolds in low-rank spiking networks
Flash talks
Day 2
David Kappel, University of Bochum (invited)
Filippo Moro
On the role of temporal hierarchy in Spiking Neural Networks
Rainer Engelken
Using Dynamical Systems Theory to Improve Surrogate Gradient Learning in Spiking Neural Networks
Anna Levina, Uni Tübingen (invited)
Ulaş İbrahim Ayyılmaz
Excitatory and inhibitory neurons exhibit distinct roles for task learning, temporal scaling, and working memory in recurrent spiking neural network models of neocortex
Veronika Koren
Efficient encoding, transmission and transformation of sensory features in a multilayer spiking network