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Found 9 papers, 1 papers with code
Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling
Our results show that the proposed approach strongly outperforms the use of backprop by up to $27\%$ higher accuracy when subject to strong hardware non-idealities.
Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation
This work proposes a novel approach for hand gesture recognition using an inexpensive, low-resolution (24 x 32) thermal sensor processed by a Spiking Neural Network (SNN) followed by Sparse Segmentation and feature-based gesture classification via Robust Principal Component Analysis (R-PCA).
Active Inference in Hebbian Learning Networks
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents.
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
In this work, we open the black box of the digital neuromorphic processor for algorithm designers by presenting the neuron processing instruction set and detailed energy consumption of the SENeCA neuromorphic architecture.
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
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.
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
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 to detect walking people on the fly using retina-inspired, event-based cameras.
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs).
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach
Currently however, people detection systems used on drones are solely based on standard cameras besides an emerging number of works discussing the fusion of imaging and event-based cameras.
A 2-$μ$J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction.
