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1. This approach enables compatibility of the same network structure with different data formats. Examples of direct coding can be found in SI Appendix, Fig. S2. 直接编码将网络的第一层视为编码层。这种方法显著减少了模拟长度同时保持准确性。对于归一化的图像 x\in [0,1]^{W\times H\times3},...
The question arises as to how these temporal properties of the brain give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Here we found a ventro-dorsal hierarchy of neural timescales that is influenced by this foraging environment. Importantly, ...
This function is then used as the loss function in a neural network. Through iterative training, the neural network minimizes this loss function until convergence, resulting in optimized transmit signal waveforms and solving the corresponding receive weighting vectors. Simulation results indicate that our...
Physics-informed neural networks (PINNs) are a class of neural networks that embed prior physical knowledge into the neural network, and have emerged as a focal area in the study of solving partial differential equations. Despite showing the significant potential in numerical simulation, PINNs still...
The other is the artificial neural network (ANN) adaptive training model improved by the adaptive genetic algorithm (AGA), which helps to improve the prediction accuracy by 80% according to experimental validation and simulation comparison. The traditional genetic algorithm (GA) has been widely used...
Network architecture The input to the model is a square grayscale image of the rat, as produced by the rat tracking module. The output consists of three pairs of Cartesian coordinates, representing the three points of nose, neck, and the base of the tail on the rat image. The model is ...
The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and envisioning of the future. While rodent studies suggest the medial temporal lobe, especially the hippocampus, i
The gradual shifting of preferred neural spiking relative to local field potentials (LFPs), known as phase precession, plays a prominent role in neural coding. Correlations between the phase precession and behavior have been observed throughout various b
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