André Grüning
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View article: Editorial: Machine Learning in Natural Complex Systems
Editorial: Machine Learning in Natural Complex Systems Open
International audience
View article: Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks
Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks Open
Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently represent sensory stimuli. A…
View article: Supervised Learning with First-to-Spike Decoding in Multilayer Spiking\n Neural Networks
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking\n Neural Networks Open
Experimental studies support the notion of spike-based neuronal information\nprocessing in the brain, with neural circuits exhibiting a wide range of\ntemporally-based coding strategies to rapidly and efficiently represent sensory\nstimuli…
View article: Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation
Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation Open
In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal that mimics backpropagation but is far less computationally in…
View article: Learning Algorithms and Signal Processing for Brain-Inspired Computing [From the Guest Editors]
Learning Algorithms and Signal Processing for Brain-Inspired Computing [From the Guest Editors] Open
The articles in this special section focuses on machine learning (ML) and signal processing algorithms for bio-inspired computing. The articles bring together key researchers in this area to provide readers of IEEE Signal Processing Magazi…
View article: An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications Open
Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking in…
View article: Implementing A New Learning Rule On Spinnaker -- Example: Brader/Fusi/Senn Rule
Implementing A New Learning Rule On Spinnaker -- Example: Brader/Fusi/Senn Rule Open
Presentation given at EITN workshop "From Neuroscience to Machine Learning". We presented here how to implement new plasticity rules in the SpiNNaker neuromorphic system as well as the first results of implementing the Brader/Fusi/Senn rul…
View article: Towards utilising the DLS v2 for Supervised Learning
Towards utilising the DLS v2 for Supervised Learning Open
Working plan on how to implement the INST/FILT rule on the prototype of the HiCANN-DLS system. This systems cannot yet read out digitally the neural membrane potentials. The only think that it can read out is STDP-like average traces of pr…
View article: Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding Open
Presentation given at the Kirchhoff-Institute at University of Heidelberg.