Backpropagation-based learning techniques for deep spiking neural networks: A survey
M Dampfhoffer, T Mesquida… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
Machine learning for tactile perception: advancements, challenges, and opportunities
The past decades have seen the rapid development of tactile sensors in material,
fabrication, and mechanical structure design. The advancement of tactile sensors has …
fabrication, and mechanical structure design. The advancement of tactile sensors has …
Spiking neural networks: A survey
The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …
increasingly accurate and robust algorithms. However, the increase in performance has …
Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding
The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …
algorithm has made significant progress in recent years. Among the various training …
[HTML][HTML] A novel out-of-distribution detection approach for spiking neural networks: design, fusion, performance evaluation and explainability
Abstract Research around Spiking Neural Networks has ignited during the last years due to
their advantages when compared to traditional neural networks, including their efficient …
their advantages when compared to traditional neural networks, including their efficient …
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task
Spiking Neural Networks (SNNs), known for their potential to enable low energy
consumption and computational cost, can bring significant advantages to the realm of …
consumption and computational cost, can bring significant advantages to the realm of …
Spiking neural networks for autonomous driving: A review
The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer
and more efficient autonomous vehicles, owing to the intricacy of modern urban …
and more efficient autonomous vehicles, owing to the intricacy of modern urban …
On the tuning of the computation capability of spiking neural membrane systems with communication on request
Spiking neural P systems (abbreviated as SNP systems) are models of computation that
mimic the behavior of biological neurons. The spiking neural P systems with communication …
mimic the behavior of biological neurons. The spiking neural P systems with communication …
Transformers in biosignal analysis: A review
Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …
Through outstanding performance in natural language processing and superior capability to …
Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing
efficiency and capabilities by following brain-inspired principles. However, the rich diversity …
efficiency and capabilities by following brain-inspired principles. However, the rich diversity …