Tdsnn: From deep neural networks to deep spike neural networks with temporal-coding
Continuous-valued deep convolutional networks (DNNs) can be converted into accurate
rate-coding based spike neural networks (SNNs). However, the substantial computational …
rate-coding based spike neural networks (SNNs). However, the substantial computational …
Deep spiking neural network: Energy efficiency through time based coding
Abstract Spiking Neural Networks (SNNs) are promising for enabling low-power event-
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …
Going deeper with directly-trained larger spiking neural networks
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …
information and event-driven signal processing, which is very suited for energy-efficient …
Training energy-efficient deep spiking neural networks with single-spike hybrid input encoding
Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional
deep learning frameworks, since they provide higher computational efficiency in event …
deep learning frameworks, since they provide higher computational efficiency in event …
Temporal-coded deep spiking neural network with easy training and robust performance
Spiking neural network (SNN) is promising but the development has fallen far behind
conventional deep neural networks (DNNs) because of difficult training. To resolve the …
conventional deep neural networks (DNNs) because of difficult training. To resolve the …
Fast and efficient information transmission with burst spikes in deep spiking neural networks
Spiking neural networks (SNNs) are considered as one of the most promising artificial
neural networks due to their energy-efficient computing capability. Recently, conversion of a …
neural networks due to their energy-efficient computing capability. Recently, conversion of a …
Temporal-coded spiking neural networks with dynamic firing threshold: Learning with event-driven backpropagation
W Wei, M Zhang, H Qu, A Belatreche… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract Spiking Neural Networks (SNNs) offer a highly promising computing paradigm due
to their biological plausibility, exceptional spatiotemporal information processing capability …
to their biological plausibility, exceptional spatiotemporal information processing capability …
Deep spiking neural network with spike count based learning rule
Deep spiking neural networks (SNNs) support asynchronous event-driven computation,
massive parallelism and demonstrate great potential to improve the energy efficiency of its …
massive parallelism and demonstrate great potential to improve the energy efficiency of its …
Dct-snn: Using dct to distribute spatial information over time for low-latency spiking neural networks
Abstract Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep
learning frameworks, since they provide higher computational efficiency due to event-driven …
learning frameworks, since they provide higher computational efficiency due to event-driven …
Exploring loss functions for time-based training strategy in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
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