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 …
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 …
Towards memory-and time-efficient backpropagation for training spiking neural networks
Abstract Spiking Neural Networks (SNNs) are promising energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the …
neuromorphic computing. For training the non-differentiable SNN models, the …
Bottom-up and top-down approaches for the design of neuromorphic processing systems: tradeoffs and synergies between natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …
Brains and bytes: Trends in neuromorphic technology
A Mehonic, J Eshraghian - APL Machine Learning, 2023 - pubs.aip.org
The term “neuromorphic” was originally introduced by Mead in the late 1980s, 1 referring to
devices and systems that imitated certain elements of biological neural systems. However …
devices and systems that imitated certain elements of biological neural systems. However …
DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
An increasing number of studies are highlighting the importance of spatial dendritic
branching in pyramidal neurons in the neocortex for supporting non-linear computation …
branching in pyramidal neurons in the neocortex for supporting non-linear computation …
Heterogeneous recurrent spiking neural network for spatio-temporal classification
B Chakraborty, S Mukhopadhyay - Frontiers in Neuroscience, 2023 - frontiersin.org
Spiking Neural Networks are often touted as brain-inspired learning models for the third
wave of Artificial Intelligence. Although recent SNNs trained with supervised …
wave of Artificial Intelligence. Although recent SNNs trained with supervised …
An energy-efficient mechanical fault diagnosis method based on neural dynamics-inspired metric SpikingFormer for insufficient samples in industrial Internet of Things
The Industrial Internet of Things (IIoT) significantly enhances mechanical fault diagnosis.
However, IIoT-based intelligent diagnostic models struggle with sample insufficiency and …
However, IIoT-based intelligent diagnostic models struggle with sample insufficiency and …
Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons
The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational
neuroscience and has been instrumental in studying our brains $\textit {in silico} $. Due to …
neuroscience and has been instrumental in studying our brains $\textit {in silico} $. Due to …
Efficient spiking neural networks with sparse selective activation for continual learning
The next generation of machine intelligence requires the capability of continual learning to
acquire new knowledge without forgetting the old one while conserving limited computing …
acquire new knowledge without forgetting the old one while conserving limited computing …