7 citations · 11 across the 8 of their papers we have counts for
8 papers
Active Inference in Hebbian Learning Networks
Ali Safa, Tim Verbelen, Lars Keuninckx +5
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative m…
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
Ali Safa, Tim Verbelen, Ilja Ocket +4
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is pr…
Learning to SLAM on the Fly in Unknown Environments: A Continual Learning Approach for Drones in Visually Ambiguous Scenes
Ali Safa, Tim Verbelen, Ilja Ocket +4
Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-bas…
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
Ali Safa, Ilja Ocket, André Bourdoux +3
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn…
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning
Ali Safa, Ilja Ocket, André Bourdoux +3
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs). In contrast to empirical parameter sear…
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach
Ali Safa, Tim Verbelen, Ilja Ocket +3
Drones are currently being explored for safety-critical applications where human agents are expected to evolve in their vicinity. In such applications, robust people avoidance must…
A 2-J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition
Ali Safa, André Bourdoux, Ilja Ocket +2
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction. Compared to traditional…
A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance
Ali Safa, Tim Verbelen, Lars Keuninckx +5
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios…