8 citations · 23 across the 13 of their papers we have counts for
7 papers · 1 filter
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler f…
Bayesian Continual Learning via Spiking Neural Networks
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engine…
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…
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…
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
This paper introduces a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (S…