6 papers
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks
Sanja Karilanova, Maxime Fabre, Emre Neftci +1
Spiking Neural Networks (SNNs) are biologically-inspired deep neural networks that efficiently extract temporal information while offering promising gains in terms of energy effici…
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…
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning
Viet Anh Khoa Tran, Emre Neftci, Willem A. M. Wybo
Biological brains learn continually from a stream of unlabeled data, while integrating specialized information from sparsely labeled examples without compromising their ability to…
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…