ResearchTrend.AI
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Bleema Rosenfeld
Osvaldo Simeone
Bipin Rajendran
ArXivPDFHTML

Papers citing "Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients"

4 / 4 papers shown
Title
A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management
A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management
Amir Saeidi
Forouzan Fallah
Soroush Barmaki
Hamed Farbeh
32
11
0
26 Mar 2022
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Hyeryung Jang
N. Skatchkovsky
Osvaldo Simeone
39
17
0
15 Dec 2020
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Guangzhi Tang
Neelesh Kumar
Raymond Yoo
Konstantinos Michmizos
54
74
0
19 Oct 2020
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
Hyeryung Jang
N. Skatchkovsky
Osvaldo Simeone
32
11
0
20 Apr 2020