no code implementations • 2 Nov 2021 • Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli.
no code implementations • 21 Feb 2021 • 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 natural language processing due to their low power profile.
no code implementations • 23 Oct 2018 • Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
In this work, the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule, whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons.