3 papers
cs.LG2021
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (S…
cs.NE2021
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning
Bleema Rosenfeld, Bipin Rajendran, Osvaldo Simeone
Spiking Neural Networks (SNNs) have recently gained popularity as machine learning models for on-device edge intelligence for applications such as mobile healthcare management and…
cs.LG2018
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
Artificial Neural Networks (ANNs) are currently being used as function approximators in many state-of-the-art Reinforcement Learning (RL) algorithms. Spiking Neural Networks (SNNs)…