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Spiking neural network

Known as: SNN, Spiking neural networks 
Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. In… 
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Related topics

Related topics

38 relations
ARM architectureAction potentialArtificial neural networkArtificial neuron

Broader (2)

Computational neuroscienceComputational statistics

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Spiking Neural Networks Hardware Implementations and Challenges
Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann… 
Highly Cited
2015
Highly Cited
2015
Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks
Broadly speaking, the goal of neuromorphic engineering is to build computer systems that mimic the brain. Spiking Neural Network… 
Review
2013
Review
2013
Evolving spiking neural network—a survey
This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its… 
Highly Cited
2010
Highly Cited
2010
Segmentation and Edge Detection Based on Spiking Neural Network Model
The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to… 
Review
2009
Review
2009
Third Generation Neural Networks: Spiking Neural Networks
Artificial Neural Networks (ANNs) are based on highly simplified brain dynamics and have been used as powerful computational… 
Review
2005
Review
2005
ReSuMe-New Supervised Learning Method for Spiking Neural Networks
In this report I introduce ReSuMe a new supervised learning method for Spiking Neural Networks. The research on ReSuMe has been… 
Highly Cited
2003
Highly Cited
2003
Spiking neural networks, an introduction
Biological neurons use short and sudden increases in voltage to send information. These signals are more commonly known as action… 
Highly Cited
2001
Highly Cited
2001
Supervised learning with spiking neural networks
We derive a supervised learning algorithm for a spiking neural network which encodes information in the timing of spike trains… 
Highly Cited
1997
Highly Cited
1997
Networks of spiking neurons: the third generation of neural network models
Highly Cited
1996
Highly Cited
1996
Lower Bounds for the Computational Power of Networks of Spiking Neurons
We investigate the computational power of a formal model for networks of spiking neurons. It is shown that simple operations on… 
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