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发表于 2025-3-21 18:45:45
书目名称Neuromorphic Cognitive Systems影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0664235<br><br> <br><br>书目名称Neuromorphic Cognitive Systems读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0664235<br><br> <br><br>
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发表于 2025-3-21 23:34:25
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Intelligent Systems Reference Libraryhttp://image.papertrans.cn/n/image/664235.jpg
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发表于 2025-3-22 11:55:46
Qiang Yu,Huajin Tang,Kay‘Tan ChenDiscusses the computational principles underlying spike-based information processing and cognitive computation with a specific focus on learning and memory.Describes theoretical modeling and analysis
Ibd810
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发表于 2025-3-23 05:07:51
Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons,g layer, followed by the final decision presented through the readout layer. The performance of the model is also analyzed and discussed. This chapter presents a general structure of SNN for pattern recognition, showing that the SNN has the ability to learn the real-world stimuli.
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发表于 2025-3-23 08:18:27
Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns,plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters.