47 citations · 132 across the 9 of their papers we have counts for
9 papers
Test-Time Adaptation of Spiking Neural Networks for Intracortical Neural Decoding using Membrane Potential Alignment
Guangzhi Tang
Intracortical brain-computer interfaces suffer from day-to-day neural signal shifts that degrade pretrained decoders. Existing unsupervised adaptation methods rely on deep recurren…
Empirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
Alberto Patiño-Saucedo, Amirreza Yousefzadeh, Guangzhi Tang +3
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANN…
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
Guangzhi Tang, Ali Safa, Kevin Shidqi +6
Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algori…
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…
Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
Guangzhi Tang, Ioannis E. Polykretis, Vladimir A. Ivanov +2
While there is still a lot to learn about astrocytes and their neuromodulatory role in the spatial and temporal integration of neuronal activity, their introduction to neuromorphic…
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM
Guangzhi Tang, Arpit Shah, Konstantinos P. Michmizos
Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of speciali…
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
Guangzhi Tang, Konstantinos P. Michmizos
It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for…