Bioscience Method 2024, Vol.15, No.1, 21-27 http://bioscipublisher.com/index.php/bm 26 The study investigated the differences in task-driven neural predictions between active and passive movements, suggesting the potential for top-down modulation at different levels of the proprioceptive pathway (Figure 7). The results showed significant differences in neural activity between active and passive movements during proprioceptive signal processing, indicating that neurons at various levels of the proprioceptive pathway might be regulated by task-related higher cognitive processes. These differences reflect the influence of cognitive and motor control factors not only in the sensory input itself but also in its processing, thus supporting the notion of top-down modulation in complex neural pathways. Figure 7 The difference in task-driven neural predictions between active and passive movement suggests a possible top-down modulation at different levels of the proprioceptive pathway 2 Analysis of Research Findings The analysis of the research results indicates that task-driven models have a superior ability to predict neural activity under active conditions compared to passive ones. This finding suggests that the model is more accurate in processing complex neural signals generated during active movements, and this accuracy is not dependent on the number of trials or their duration, indicating good generalization capability of the model. Comparing task-driven models with data-driven models further revealed the superiority of the former in capturing and explaining neural activities. Task-driven models are better at elucidating how motor control and sensory input are integrated and processed in the brain, whereas data-driven models show limitations in explaining complex neural activities. These comparative results emphasize the importance of adopting task-driven approaches in understanding neural encoding and processing mechanisms in neuroscience research. 3 Evaluation of the Research The results of the research demonstrates the significant advantages of the task-driven modeling approach in understanding and predicting the neural dynamics of proprioception. This method, combined with large-scale synthetic datasets, effectively simulates the motor and sensory processing mechanisms of organisms, providing a powerful tool for in-depth analysis of the functioning of the neural system. The study not only proves the effectiveness of deep learning technologies in processing and interpreting complex neural data but also reveals the subtle dynamics in the proprioceptive process through musculoskeletal modeling. This integrated approach underscores the critical role of computational models in decoding the functions of the neural system, particularly in simulating neural activities and proprioceptive responses. Moreover, this study's methodology offers a robust framework for future exploration of unresolved mysteries in neuroscience, especially in understanding how complex neural networks respond to different types of sensory inputs and motor control tasks. 4 Conclusions This study explicitly demonstrates that task-driven neural network models can effectively predict the neural dynamics of proprioception in primates. With their exceptional performance under active movement conditions, these models highlight their potent ability to simulate and understand the mechanisms of proprioception. Through an in-depth analysis of various types of task-driven models, the research reveals how these models more accurately capture and explain the complex dynamics of the neural system, especially when processing neural signals generated during active movements. Moreover, comparing the performance of different models emphasizes the importance of the task-driven approach in precisely predicting neural activity and indicates that this method can provide profound insights into how the brain integrates sensory input and motor control. Overall,
RkJQdWJsaXNoZXIy MjQ4ODYzMg==