29 citations · 65 across the 6 of their papers we have counts for
5 papers · 1 filter
Understanding the Convergence in Balanced Resonate-and-Fire Neurons
Saya Higuchi, Sander M. Bohte, Sebastian Otte
Resonate-and-Fire (RF) neurons are an interesting complementary model for integrator neurons in spiking neural networks (SNNs). Due to their resonating membrane dynamics they can e…
Balanced Resonate-and-Fire Neurons
Saya Higuchi, Sebastian Kairat, Sander M. Bohte +1
The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns wi…
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Bojian Yin, Federico Corradi, Sander M. Bohte
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful mo…
A Biologically Plausible Learning Rule for Deep Learning in the Brain
Isabella Pozzi, Sander Bohté, Pieter Roelfsema
Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain.…
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Davide Zambrano, Roeland Nusselder, H. Steven Scholte +1
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in AN…