Regime switching in a time series is an important and challenging issue in complex financial system analysis. Existing regime models have focused on the features of fluctuations at a single point in financial time series, often neglecting time series...
Molecular circuits capable of autonomous learning could unlock novel applications in fields such as bioengineering and synthetic biology. To this end, existing chemical implementations of neural computing have primarily relied on emulating discrete-l...
With the increasing demand for wind energy in the electric power generation industry, optimizing robust and efficient control strategies is essential for a wind energy conversion system (WECS). In this regard, this study proposes a novel hybrid contr...
Proceedings of the National Academy of Sciences of the United States of America
Jul 31, 2025
Computational models of reinforcement learning (RL) have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, potentially ...
We propose a mathematical framework to systematically explore the propagation properties of a class of continuous in time nonlinear neural network models comprising a hierarchy of processing areas, mutually connected according to the principles of pr...
Recurrent neural circuits often face inherent complexities in learning and generating their desired outputs, especially when they initially exhibit chaotic spontaneous activity. While the celebrated FORCE learning rule can train chaotic recurrent net...
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However...
Neural networks : the official journal of the International Neural Network Society
Jun 18, 2025
Under the framework of backstepping theory, dealing with the non-differentiable problem of virtual control signals caused by sensor output triggering is difficult. Meanwhile, it is of great practical significance to consider problems of output trigge...
BACKGROUND: Numerous studies have revealed abnormalities in EEG microstate in insomnia, primarily quantified using linear features, whereas nonlinear metrics remain underexplored. This study aimed to compare linear and nonlinear features and further ...
Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many tr-FC approaches have been proposed, most are linear approaches, e.g. ...
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