76 citations · 185 across the 6 of their papers we have counts for
9 papers
Embodied Synaptic Plasticity with Online Reinforcement learning
Jacques Kaiser, Michael Hoff, Andreas Konle +8
The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in…
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikes
Christoph Stöckl, Wolfgang Maass
Spike-based neuromorphic hardware promises to reduce the energy consumption of image classification and other deep learning applications, particularly on mobile phones or other edg…
Recognizing Images with at most one Spike per Neuron
Christoph Stöckl, Wolfgang Maass
In order to port the performance of trained artificial neural networks (ANNs) to spiking neural networks (SNNs), which can be implemented in neuromorphic hardware with a drasticall…
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…