227 citations · 823 across the 17 of their papers we have counts for
17 papers
Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
Carlo Wenig, Raoul-Martin Memmesheimer, Christian Klos
The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing. However, for the extensively used leaky int…
A generalized neural tangent kernel for surrogate gradient learning
Luke Eilers, Raoul-Martin Memmesheimer, Sven Goedeke
State-of-the-art neural network training methods depend on the gradient of the network function. Therefore, they cannot be applied to networks whose activation functions do not hav…
Smooth Exact Gradient Descent Learning in Spiking Neural Networks
Christian Klos, Raoul-Martin Memmesheimer
Gradient descent prevails in artificial neural network training, but seems inept for spiking neural networks as small parameter changes can cause sudden, disruptive (dis-)appearanc…
From Single Neurons to Behavior in the Jellyfish Aurelia aurita
Fabian Pallasdies, Sven Goedeke, Wilhelm Braun +1
Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earl…
Dynamical learning of dynamics
Christian Klos, Yaroslav Felipe Kalle Kossio, Sven Goedeke +2
The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification. Here we sho…
Dynamics and computation in mixed networks containing neurons that accelerate towards spiking
Paul Manz, Sven Goedeke, Raoul-Martin Memmesheimer
Networks in the brain consist of different types of neurons. Here we investigate the influence of neuron diversity on the dynamics, phase space structure and computational capabili…