90 citations · 101 across the 7 of their papers we have counts for
15 papers
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
Nicolas Skatchkovsky, Osvaldo Simeone, Hyeryung Jang
One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or imag…
Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization
Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of the efficiency of biological brains by processing through binary neural dynamic activation…
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the…
Spiking Neural Networks -- Part III: Neuromorphic Communications
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of m…
Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Inspired by the operation of biological brains, Spiking Neural Networks (SNNs) have the unique ability to detect information encoded in spatio-temporal patterns of spiking signals.…
Spiking Neural Networks -- Part I: Detecting Spatial Patterns
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driv…