Bioscience Method 2024, Vol.15, No.1, 21-27 http://bioscipublisher.com/index.php/bm 24 Figure 4 Task-driven neural network models predict neural dynamics during active and passive movements The study also compared hypotheses based on kinematic tasks in terms of developing features closest to the brain (Figure 5). By comparing the top 3% of models in all computational tasks, Figure 5A shows the distribution of neural explainability for NHP S (CN) and NHP H (S1), while contrasting the predictions of randomly initialized untrained models and linear models. Figure 5B used UMAP embedding space to visualize the difference in neural explainability between task-driven and untrained networks, with each data point representing a group of networks trained for a given computational task. Figure 5C compared the explained variance gains of all neural network models trained on the hand position and velocity task against linear models and the corresponding untrained models. For passive predictions, 5D, 5E, and 5F are similar to the above, showing the analysis results under passive conditions. These results highlight the superiority of task-driven models in predicting neural dynamics compared to linear and untrained models. Figure 6 indicates a significant correlation between neural network task performance and the model's neural explainability. By analyzing the 10-layer task-driven model, the distribution of the best-predicting layer for each neuron is shown, with different colors representing different NHPs (Figure 6A). Figure 6C reveals the relationship between explained variance and task performance (mean squared error, MSE) for all TCN models (N = 300) trained on the hand position and velocity (HP and HV) tasks, displaying a negative correlation, meaning lower MSE indicates better performance. Figures 6E and 6F show the relationship between explained variance and task performance for trained and untrained models during active and passive movements, respectively, pointing out that trained models have better predictive capabilities than untrained models. Figures 6G and 6H are linear fits for all computational tasks, showing the performance of NHP S during active movement, with error metrics Z-scored for task comparison, where higher values indicate poorer performance.
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