Training spiking neural networks with event-driven backpropagation
Abstract Spiking Neural networks (SNNs) represent and transmit information by
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …
Rectified linear postsynaptic potential function for backpropagation in deep spiking neural networks
Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit
information, which are not only biologically realistic but also suitable for ultralow-power …
information, which are not only biologically realistic but also suitable for ultralow-power …
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes)
which can potentially lead to higher energy-efficiency in neuromorphic hardware …
which can potentially lead to higher energy-efficiency in neuromorphic hardware …
Training spiking neural networks with accumulated spiking flow
The fast development of neuromorphic hardwares promotes Spiking Neural Networks
(SNNs) to a thrilling research avenue. Current SNNs, though much efficient, are less …
(SNNs) to a thrilling research avenue. Current SNNs, though much efficient, are less …
[HTML][HTML] SSTDP: Supervised spike timing dependent plasticity for efficient spiking neural network training
Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power
event-driven neuromorphic hardware due to their spatio-temporal information processing …
event-driven neuromorphic hardware due to their spatio-temporal information processing …
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 …
Online training through time for spiking neural networks
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models.
Recent progress in training methods has enabled successful deep SNNs on large-scale …
Recent progress in training methods has enabled successful deep SNNs on large-scale …
Real spike: Learning real-valued spikes for spiking neural networks
Y Guo, L Zhang, Y Chen, X Tong, X Liu… - … on Computer Vision, 2022 - Springer
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more
attention due to their event-driven and energy-efficient characteristics. The integration of …
attention due to their event-driven and energy-efficient characteristics. The integration of …
[HTML][HTML] Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …