76 citations · 201 across the 8 of their papers we have counts for
8 papers · 1 filter
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware
Philipp Plank, Arjun Rao, Andreas Wild +1
Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this re…
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…