[HTML][HTML] A survey of encoding techniques for signal processing in spiking neural networks
Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …
intelligence due to their ability to solve complex problems while being power efficient. They …
Neuromorphic engineering: from biological to spike‐based hardware nervous systems
JQ Yang, R Wang, Y Ren, JY Mao, ZP Wang… - Advanced …, 2020 - Wiley Online Library
The human brain is a sophisticated, high‐performance biocomputer that processes multiple
complex tasks in parallel with high efficiency and remarkably low power consumption …
complex tasks in parallel with high efficiency and remarkably low power consumption …
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 …
Memristor‐Based Neuromorphic Chips
X Duan, Z Cao, K Gao, W Yan, S Sun… - Advanced …, 2024 - Wiley Online Library
In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …
Deep learning in spiking neural networks
A Tavanaei, M Ghodrati, SR Kheradpisheh… - Neural networks, 2019 - Elsevier
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
[HTML][HTML] Enabling spike-based backpropagation for training deep neural network architectures
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing
paradigm. However, the typical shallow SNN architectures have limited capacity for …
paradigm. However, the typical shallow SNN architectures have limited capacity for …
Neuromorphic computing using non-volatile memory
Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …
for implementing massively-parallel and highly energy-efficient neuromorphic computing …
STDP-based spiking deep convolutional neural networks for object recognition
SR Kheradpisheh, M Ganjtabesh, SJ Thorpe… - Neural Networks, 2018 - Elsevier
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in
spiking neural networks (SNN) to extract visual features of low or intermediate complexity in …
spiking neural networks (SNN) to extract visual features of low or intermediate complexity in …
[HTML][HTML] Unsupervised learning of digit recognition using spike-timing-dependent plasticity
In order to understand how the mammalian neocortex is performing computations, two
things are necessary; we need to have a good understanding of the available neuronal …
things are necessary; we need to have a good understanding of the available neuronal …
Minerva: Enabling low-power, highly-accurate deep neural network accelerators
The continued success of Deep Neural Networks (DNNs) in classification tasks has sparked
a trend of accelerating their execution with specialized hardware. While published designs …
a trend of accelerating their execution with specialized hardware. While published designs …