File S1 Contains: - A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

Figure S1. Final prediction results compared to the synaptic parameters (rational model). Comparison between prediction errors obtained using the 4-by-4 rational curve fitting model and the different synaptic parameters. (A): Comparison to RMSE. (B): Comparison to R2. Figure S2. Final prediction results compared to the synaptic parameters (Fourier model). Comparison between prediction errors obtained using the Fourier curve fitting model and the different synaptic parameters. (A): Comparison to RMSE. (B): Comparison to R2. Table S1. Curve fitting test results. Average curve fitting test results for all possible curve fitting alternatives tested against the Monte Carlo simulation data. Table S2. Machine learning techniques evaluation results: RMSE. Comparison of validation results (RMSE) obtained during the 10-fold cross-validation process for all machine learning techniques tested. Table S3. Machine learning techniques evaluation result: R2. Comparison of validation results (R2) obtained during the 10-fold cross-validation process for all machine learning techniques tested. Table S4. Linear correlation between synapse parameters and function coefficients: 4-by-4 degree polynomial rational function. Observed linear correlation between synapse parameters and coefficients of the 4-by-4 degree polynomial rational function for all Monte Carlo synapse simulations. Table S5. Linear correlation between synapse parameters and function coefficients: 8-term Fourier series. Observed linear correlation between synapse parameters and coefficients of the 8-term Fourier series for all Monte Carlo synapse simulations. Table S6 Linear correlation between synapse parameters and function coefficients: 8-term Gauss series. Observed linear correlation between synapse parameters and coefficients of the 8-term Gauss series for all Monte Carlo synapse simulations. Table S7. Linear correlation between synapse parameters and function coefficients: 2-term exponential function. Observed linear correlation between synapse parameters and coefficients of the 2-term exponential function for all Monte Carlo synapse simulations. Table S8. Linear correlation between synapse parameters and function coefficients: 9-degree polynomial. Observed linear correlation between synapse parameters and coefficients of the 9-degree polynomial function for all Monte Carlo synapse simulations. Table S9. Final prediction results. Comparison between Monte Carlo simulations and prediction results obtained for all 1000 synapse simulations, using the 4-by-4 rational and Fourier curve fitting models. (A): Detailed results. (B): Average and stdev values. Table S10. Correlation between synaptic parameters and AMPA activated receptors curve peak. Pearson's linear correlation coefficient between the AMPA activated receptors curve peak and the values of the different synaptic parameters. Table S11. Final prediction results of the extended experiment. Comparison between Monte Carlo simulations and prediction results obtained for all 2000 synapse simulations, using the 4-by-4 rational and Fourier curve fitting models. (A): Detailed results. (B): Average and stdev values. (XLSX)