Convolutional spike-triggered covariance analysis for neural subunit models

Keyphrases

• Analysis of Covariance 100%
• Spike-triggered Covariance 100%
• Convolutional 100%
• Nonlinearity 66%
• Local Optimum 33%
• Performance Prediction 33%
• Model Mismatch 33%
• Maximum Likelihood Estimator 33%
• Complex Stimuli 33%
• Neural Response 33%
• Primary Visual Cortex 33%
• Computational Challenges 33%
• Macaque 33%
• Neural Data 33%
• Sensory Response 33%
• Log-likelihood Ratio 33%
• Covariance Matrix 33%
• Spike-triggered Average 33%
• Asymptotically Efficient Estimator 33%
• Quadratic Model 33%
• Biological Plausibility 33%
• Linear Convolution 33%
• Linear/non-linear 33%
• Nonlinear Stage 33%
• Moment-based Estimators 33%
• Averaging Matrix 33%

Mathematics

• Covariance 100%
• Nonlinearity 100%
• Local Optimum 50%
• Maximum Likelihood Estimator 50%
• Covariance Matrix 50%
• Convolution 50%
• Identifiability 50%
• Quadratic Model 50%
• Log Likelihood 50%
• Plausibility 50%
• Primary Visual Cortex 50%