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The brain’s functional connectome continually rewires throughout an organism’s life. In this study, we sought to elucidate the operational principles of such rewiring in mouse primary motor cortex (M1) by analyzing calcium imaging of layer 2/3 (L2/3) and layer 5 (L5) neuronal activity in M1 of awake mice during a lever-press task learning. Our results show that L2/3 and L5 functional connectomes follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with improved motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward/performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning. 

Deep learning models have achieved remarkable accuracy for structural response modeling. However, these models heavily depend on having a sufficient amount of training data, which can be challenging and time-consuming to collect. Moreover, data-driven models sometimes struggle to adhere to physics constraints. Therefore, in this study, a physics-informed long short-term memory (PI-LSTM) network was applied to structural response modeling by incorporating physics constraints into deep learning. The physics constraints were modified to accommodate the characteristics of both linear and nonlinear cases. The PI-LSTM network, inspired by and compared with existing physics-informed deep learning models (PhyCNN and PhyLSTM), was validated using the numerical simulation results of the single-degree-of-freedom (SDOF) system and the experimental results of the six-story building. Additionally, the PI-LSTM network underwent thorough investigation and validation across the four cases of the SDOF system and numerical simulation results of the six-story building with the comparison of the regular LSTM. The results indicate that the PI-LSTM network outperformed the regular LSTM models in terms of accuracy. Furthermore, the PI-LSTM network exhibited a more concentrated and higher accuracy range when analyzing the results of both the SDOF system and the six-story building. These findings demonstrate that the PI-LSTM network presents a reliable and efficient approach for structural response modeling. 

Deep transfer learning (TL) has great potential for a wide range of applications in civil engineering. This work aims to propose a deep transfer learning-based method for vehicle classification by asphalt pavement vibration. This work first used the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. The vibration signals were then represented in two different ways, including the time-domain graph and the time-frequency graph. Finally, two deep transfer learning-based methods, namely Method Ⅰ (Time-domain & TL) and Method Ⅱ (Time-frequency & TL), were applied for vehicle classification according to the two different representations of vibration signals. The results show that the CNN model had a satisfactory performance in both methods with the accuracy of Method Ⅰ exceeding 0.94 and Method Ⅱ exceeding 0.95. The CNN model in Method Ⅱ performed better in the accuracy metrics with considering label imbalance, but worse in the accuracy metrics without considering label imbalance than that in Method Ⅰ. The differences between these two methods have been investigated and discussed in detail in terms of input types, accuracy metrics, and application prospects. The CNN model with deep transfer learning could be an effective, accurate, and reliable technique for vehicle classification based on asphalt pavement vibration.