This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed
The most biologically-inspired artificial neurons are those of the third generation, and are termed ...
Motivated by the desire to better understand the truly remarkable information processing capabilitie...
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signa...
Artificial neural networks have been used as a powerful processing tool in various areas such as pat...
International audienceSpiking Neuron Networks (SNNs) are often referred to as the 3rd generation ofn...
Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and ...
Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Hig...
The past decade has witnessed the great success of deep neural networks in various domains. However,...
This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes...
International audienceComputational neuroscience is an appealing interdisciplinary domain, at the in...
In the preceding chapter a number of mathematical models for spiking neurons were introduced. Spikin...
This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) ...
Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators fo...
We have investigated an existing theoretical model for spiking neural networks, and based on this mo...
Abstract-The paper presents a novel, biologically plausible spiking neuronal model that includes a d...
The most biologically-inspired artificial neurons are those of the third generation, and are termed ...
Motivated by the desire to better understand the truly remarkable information processing capabilitie...
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signa...
Artificial neural networks have been used as a powerful processing tool in various areas such as pat...
International audienceSpiking Neuron Networks (SNNs) are often referred to as the 3rd generation ofn...
Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and ...
Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Hig...
The past decade has witnessed the great success of deep neural networks in various domains. However,...
This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes...
International audienceComputational neuroscience is an appealing interdisciplinary domain, at the in...
In the preceding chapter a number of mathematical models for spiking neurons were introduced. Spikin...
This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) ...
Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators fo...
We have investigated an existing theoretical model for spiking neural networks, and based on this mo...
Abstract-The paper presents a novel, biologically plausible spiking neuronal model that includes a d...
The most biologically-inspired artificial neurons are those of the third generation, and are termed ...
Motivated by the desire to better understand the truly remarkable information processing capabilitie...
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signa...