In many cases, neurons process information carried by the precise timings of spikes. Here
we show how neurons can learn to generate specific temporally precise output spikes in …

Spiking neural networks (SNN) have gained popularity in embedded applications such as
robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease …

Frequency instability has been a growing problem in recent years due to the rising
penetration of distributed generating systems in the format of Microgrids (MGs), which are …

A supervised learning rule for Spiking Neural Networks (SNNs) is presented that can cope
with neurons that spike multiple times. The rule is developed by extending the existing …

Hardware implementations of Spiking Neural Networks are numerous because they are well
suited for implementation in digital and analog hardware, and outperform classic neural …

脉冲神经网络是进行复杂时空信息处理的有效工具, 但由于其内在的不连续和非线性机制,
构建高效的脉冲神经网络监督学习算法非常困难, 同时也是该研究领域的重要问题 …

Artificial neural networks are used in various fields of science: from speech recognition
systems to recognition of the secondary protein structure, classification of various types of …

Recent investigation of cortical coding and computation indicates that temporal coding is
probably a more biologically plausible scheme used by neurons than the rate coding used …

Spiking Neural Networks (SNN) consist of fully interconnected computation units (neurons)
based on spike processing. This type of networks resembles those found in biological …

For years of researches and developments in the field of neurotechnologies a big variety of
the artificial neural networks somewhat reminding work of biological neural systems was …