The maximum points-based supervised learning rule for spiking neural networks

DOI10.1007/s00500-018-3576-0zbMath1430.68279OpenAlexW2898927016WikidataQ129009225 ScholiaQ129009225MaRDI QIDQ2297998

Hong Qu, Qing Cai, Malu Zhang, Guisong Liu, Xiurui Xie

Publication date: 20 February 2020

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-018-3576-0

zbMATH Keywords

supervised learningspiking neural networksback propagation mechanismtemporal encoding mechanism

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Nature-inspired metaheuristic algorithms to find near-OGR sequences for WDM channel allocation and their performance comparison
• The evidence for neural information processing with precise spike-times: A survey
• Error-backpropagation in temporally encoded networks of spiking neurons
• A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
• A Novel Spike Distance
• EVOLUTIONARY DESIGN OF SPIKING NEURAL NETWORKS
• Competitive STDP-Based Spike Pattern Learning
• Metric-space analysis of spike trains: theory, algorithms and application
• Spiking Neuron Models
• Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
• Supervised Learning in Multilayer Spiking Neural Networks
• A New Supervised Learning Algorithm for Spiking Neurons

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