162 citations · 783 across the 23 of their papers we have counts for
4 papers · 1 filter
Reservoirs learn to learn
Anand Subramoney, Franz Scherr, Wolfgang Maass
We consider reservoirs in the form of liquid state machines, i.e., recurrently connected networks of spiking neurons with randomly chosen weights. So far only the weights of a line…
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Yexin Yan, David Kappel, Felix Neumaerker +7
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost…
Neuromorphic Hardware learns to learn
Thomas Bohnstingl, Franz Scherr, Christian Pehle +2
Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand. In contrast, the hyperparameters and learning algorithms of networks of neurons in the…
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
Guillaume Bellec, Franz Scherr, Elias Hajek +3
The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack o…