Learning and recognition of multiple fluctuating temporal patterns using S-CTRNN

Mathematics

• Learning 78%
• Continuous Time 74%
• Recurrent Neural Networks 55%
• Training 45%
• Maximum Likelihood Estimation 40%
• Gradient Descent Method 31%
• Neural Network Model 26%
• Gaussian Noise 25%
• Neuron 22%
• Predict 20%
• Target 18%
• Numerical Experiment 15%
• Context 13%
• Demonstrate 13%

Engineering & Materials Science

• Maximum likelihood estimation 100%
• Recurrent neural networks 90%
• Steepest descent method 54%
• Stochastic models 47%
• Neurons 39%
• Experiments 15%