76 citations · 160 across the 6 of their papers we have counts for
6 papers
Self-Supervised Learning Through Efference Copies
Franz Scherr, Qinghai Guo, Timoleon Moraitis
Self-supervised learning (SSL) methods aim to exploit the abundance of unlabelled data for machine learning (ML), however the underlying principles are often method-specific. An SS…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince, Roy Henha Eyono, Ellen Boven +9
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based l…
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…
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…