Robust learning in SpikeProp
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Publication:2281710
DOI10.1016/j.neunet.2016.10.011zbMath1428.68254OpenAlexW2554917868WikidataQ39153549 ScholiaQ39153549MaRDI QIDQ2281710
Publication date: 6 January 2020
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.neunet.2016.10.011
robust stabilityerror analysissupervised learningspiking neural networkweight convergenceadaptive learning rate
Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (3)
Robust spike-train learning in spike-event based weight update ⋮ SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks ⋮ On the Algorithmic Power of Spiking Neural Networks
Cites Work
- Adaptive learning rate of SpikeProp based on weight convergence analysis
- A gradient descent rule for spiking neurons emitting multiple spikes
- Error-backpropagation in temporally encoded networks of spiking neurons
- A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
- Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex
- Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
- Robustness of discrete-time direct adaptive controllers
- Robustness analysis of discrete-time adaptive control systems using input-output stability theory: a tutorial
- Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
- Spiking Neuron Models
- Lower Bounds for the Computational Power of Networks of Spiking Neurons
- Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
- Supervised Learning in Multilayer Spiking Neural Networks
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