149 citations · 154 across the 3 of their papers we have counts for
5 papers
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
Tengjun Liu, Julia Gygax, Julian Rossbroich +3
Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection…
Brain-Inspired Learning on Neuromorphic Substrates
Friedemann Zenke, Emre O. Neftci
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solv…
Finding trainable sparse networks through Neural Tangent Transfer
Tianlin Liu, Friedemann Zenke
Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial. The requirements of real biological neural networks are ra…
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel +1
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these network…
Surrogate Gradient Learning in Spiking Neural Networks
Emre O. Neftci, Hesham Mostafa, Friedemann Zenke
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neu…