6 papers
Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation
Federica Ferrari, Flavia Davidhi, Bernard Maacaron +6
Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mech…
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4
On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, r…
A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks
Matteo Saponati, Chiara De Luca, Giacomo Indiveri +1
Unlike traditional artificial neural networks (ANNs), biological neuronal networks solve complex cognitive tasks with sparse neuronal activity, recurrent connections, and local lea…
Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning
Irene Ambrosini, Ingo Blakowski, Dmitrii Zendrikov +5
Air hockey demands split-second decisions at high puck velocities, a challenge we address with a compact network of spiking neurons running on a mixed-signal analog/digital neuromo…
A neuromorphic continuous soil monitoring system for precision irrigation
Mirco Tincani, Khaled Kerouch, Umberto Garlando +4
Sensory processing at the edge requires ultra-low power stand-alone computing technologies. This is particularly true for modern agriculture and precision irrigation systems which…
Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Chiara De Luca, Elisa Donati
Queen bee presence is essential for the health and stability of honeybee colonies, yet current monitoring methods rely on manual inspections that are labor-intensive, disruptive, a…