19 citations · 41 across the 7 of their papers we have counts for
10 papers
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons
Mattias Nilsson, Ton Juny Pina, Lyes Khacef +3
With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive…
ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware
Fernando M. Quintana, Fernando Perez-Peña, Pedro L. Galindo +3
Neuromorphic perception with event-based sensors, asynchronous hardware and spiking neurons is showing promising results for real-time and energy-efficient inference in embedded sy…
Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition
Mohamed Sadek Bouanane, Dalila Cherifi, Elisabetta Chicca +1
Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, mult…
Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits
Lyes Khacef, Philipp Klein, Matteo Cartiglia +3
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptiv…
Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware
Simon F Muller-Cleve, Vittorio Fra, Lyes Khacef +8
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstan…
A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models
Artem R. Muliukov, Laurent Rodriguez, Benoit Miramond +4
The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is…
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
Lyes Khacef, Vincent Gripon, Benoit Miramond
Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur…
Improving Self-Organizing Maps with Unsupervised Feature Extraction
Lyes Khacef, Laurent Rodriguez, Benoit Miramond
The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn…
Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
Lyes Khacef, Laurent Rodriguez, Benoit Miramond
Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural…
Neuromorphic hardware as a self-organizing computing system
Lyes Khacef, Bernard Girau, Nicolas Rougier +2
This paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibil…