19 citations · 23 across the 11 of their papers we have counts for
11 papers
Learning sequence timing and control of replay speed in networks of spiking neurons
Melissa Lober, Younes Bouhadjar, Markus Diesmann +1
Processing sequential inputs is a fundamental brain function, underlying tasks such as sensory perception, language, and motor control. A challenge in sequence processing is to rep…
Sparse Axonal and Dendritic Delays Enable Competitive SNNs for Keyword Classification
Younes Bouhadjar, Emre Neftci
Training transmission delays in spiking neural networks (SNNs) has been shown to substantially improve their performance on complex temporal tasks. In this work, we show that learn…
Dissecting Linear Recurrent Models: How Different Gating Strategies Drive Selectivity and Generalization
Younes Bouhadjar, Maxime Fabre, Felix Schmidt +1
Linear recurrent neural networks have emerged as efficient alternatives to the original Transformer's softmax attention mechanism, thanks to their highly parallelizable training an…
SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets
Barna Zajzon, Younes Bouhadjar, Maxime Fabre +5
Sequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property…
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
Sebastian Siegel, Ming-Jay Yang, Younes Bouhadjar +3
Structured State Space models (SSM) have recently emerged as a new class of deep learning models, particularly well-suited for processing long sequences. Their constant memory foot…
SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
Maxime Fabre, Lyubov Dudchenko, Younes Bouhadjar +1
Multi-state spiking neurons combine sparse binary activations with rich second-order nonlinear recurrent dynamics, making them a promising alternative to standard deep learning mod…
Unsupervised Learning of Spatio-Temporal Patterns in Spiking Neuronal Networks
Florian Feiler, Emre Neftci, Younes Bouhadjar
The ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain managem…
Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training
Hadjer Benmeziane, Amine Ziad Ounnoughene, Imane Hamzaoui +1
Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and th…
Sequence learning in a spiking neuronal network with memristive synapses
Younes Bouhadjar, Sebastian Siegel, Tom Tetzlaff +3
Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, effic…
Coherent noise enables probabilistic sequence replay in spiking neuronal networks
Younes Bouhadjar, Dirk J. Wouters, Markus Diesmann +1
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequentl…
Sequence learning, prediction, and replay in networks of spiking neurons
Younes Bouhadjar, Dirk J. Wouters, Markus Diesmann +1
Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm re…