5 citations · 5 across the 2 of their papers we have counts for
3 papers · 1 filter
BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks
Guangzhi Tang, Neelesh Kumar, Ioannis Polykretis +1
Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic chips. To harn…
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Guangzhi Tang, Neelesh Kumar, Raymond Yoo +1
The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, wh…
Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware
Guangzhi Tang, Neelesh Kumar, Konstantinos P. Michmizos
Energy-efficient mapless navigation is crucial for mobile robots as they explore unknown environments with limited on-board resources. Although the recent deep reinforcement learni…