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 …
A review of learning in biologically plausible spiking neural networks
Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
STDP-based spiking deep convolutional neural networks for object recognition
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 …
Spiking neural networks and online learning: An overview and perspectives
Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …
increasingly prevalent, being therefore necessary to learn in an online manner. These …
A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS
Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
Temporal backpropagation for spiking neural networks with one spike per neuron
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs)
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …
Supervised learning based on temporal coding in spiking neural networks
H Mostafa - IEEE transactions on neural networks and learning …, 2017 - ieeexplore.ieee.org
Gradient descent training techniques are remarkably successful in training analog-valued
artificial neural networks (ANNs). Such training techniques, however, do not transfer easily …
artificial neural networks (ANNs). Such training techniques, however, do not transfer easily …
[書籍][B] Transfer entropy
Transfer Entropy Page 1 Chapter 4 Transfer Entropy In this chapter we get to the essential
mathematics of the book—a detailed discussion of transfer entropy. To begin with we look at …
mathematics of the book—a detailed discussion of transfer entropy. To begin with we look at …
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 …