Multi-sample online learning for spiking neural networks based on generalized expectation maximization

Keyphrases

• Spiking Neural Networks 100%
• Online Learning 100%
• Log-likelihood Ratio 100%
• Generalized Expectation Maximization 100%
• Neural Network-based 100%
• Multisample 100%
• Classification Task 25%
• Single Sample 25%
• Spike Signal 25%
• Tight 25%
• Neural Dynamics 25%
• Online Learning Algorithm 25%
• Single Run 25%
• Importance Sampling 25%
• Desirable Output 25%
• MNIST 25%
• Multi-compartment 25%
• Statistical Estimation 25%
• Unbiased Estimate 25%
• Probabilistic Spiking Neural Networks 25%
• Sample Estimator 25%
• Biological Brain 25%
• Learning Signals 25%
• Computational Paradigm 25%
• Training Criterion 25%
• Three-factor Rules 25%
• Spiking Neural Network Model 25%
• Synaptic Weight 25%
• Neuromorphic 25%
• Dynamic Activation 25%

Computer Science

• Neural Network 100%
• Electronic Learning 100%
• Classification Task 50%
• Approximation (Algorithm) 50%
• Learning Algorithm 50%
• Experimental Result 50%
• Neural Network Model 50%
• Importance Sampling 50%
• Synaptic Weight 50%
• Independent Sample 50%

Mathematics

• Neural Network 100%
• Log Likelihood 100%
• Single Run 25%
• Importance Sampling 25%
• Network Model 25%
• Synaptic Weight 25%
• Neural Dynamic 25%
• Statistical Estimate 25%
• Independent Sample 25%
• Likelihood 25%