The chronotron: A neuron that learns to fire temporally precise spike patterns
RV Florian - 2012 - journals.plos.org
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 …
we show how neurons can learn to generate specific temporally precise output spikes in …
Simplified spiking neural network architecture and STDP learning algorithm applied to image classification
T Iakymchuk, A Rosado-Muñoz… - EURASIP Journal on …, 2015 - Springer
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 …
robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease …
Neuromorphic deep learning frequency regulation in stand-alone microgrids
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 …
penetration of distributed generating systems in the format of Microgrids (MGs), which are …
A gradient descent rule for spiking neurons emitting multiple spikes
O Booij, H tat Nguyen - Information Processing Letters, 2005 - Elsevier
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 …
with neurons that spike multiple times. The rule is developed by extending the existing …
Compact hardware liquid state machines on FPGA for real-time speech recognition
B Schrauwen, M D'Haene, D Verstraeten… - Neural networks, 2008 - Elsevier
Hardware implementations of Spiking Neural Networks are numerous because they are well
suited for implementation in digital and analog hardware, and outperform classic neural …
suited for implementation in digital and analog hardware, and outperform classic neural …
[PDF][PDF] 脉冲神经网络的监督学习算法研究综述
蔺想红, 王向文, 张宁, 马慧芳 - 电子学报, 2015 - ejournal.org.cn
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 …
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 …
Analysis of perspective models of artificial neural networks for control of robotic objects
AV Popov, KS Sayarkin… - 2018 IEEE conference of …, 2018 - ieeexplore.ieee.org
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 …
systems to recognition of the secondary protein structure, classification of various types of …
Spiking neural networks for cortical neuronal spike train decoding
H Fang, Y Wang, J He - Neural Computation, 2010 - direct.mit.edu
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 …
probably a more biologically plausible scheme used by neurons than the rate coding used …
Fast spiking neural network architecture for low-cost FPGA devices
Spiking Neural Networks (SNN) consist of fully interconnected computation units (neurons)
based on spike processing. This type of networks resembles those found in biological …
based on spike processing. This type of networks resembles those found in biological …
Synthesis of model of hardware realization of LIF-model of biological neuron on the basis of FPGA
LO Slepova, AA Zhilenkov - 2018 IEEE Conference of Russian …, 2018 - ieeexplore.ieee.org
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 …
the artificial neural networks somewhat reminding work of biological neural systems was …