Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...
Moreover, from a functional perspective, certain classes of recurrent networks can ... the transmission of neural activity. Because the brain is a highly modular structure, it is vitally important ...
To address the above problems, this article proposes a recurrent self-coding neural network (RSCN) for one-stop noise cancellation ... The self-encoding structure is updated cyclically to realize ...
Unlike traditional recurrent neural networks and Convolutional Networks ... Furthermore, the sparse connectivity structure reduces computational complexity and enhances the interpretability of the ...
Binjumah, W. (2025) A Conceptual Model for Improving Perovskite Solar Cells Efficiency Using Machine Learning. International ...
This important work substantially advances our understanding of episodic memory by proposing a biologically plausible mechanism through which hippocampal barcode activity enables efficient memory ...
This work models reinforcement-learning experiments using a recurrent neural network. It examines if the detailed credit assignment necessary for back-propagation through time can be replaced with ...
4don MSN
Neural network models that are able to make decisions or store memories have long captured scientists' imaginations. In these ...
Researchers developed a novel method combining hydrogel-coated microfluidic devices with high-density microelectrode arrays ...
Do speakers of different languages build sentence structure in the same way? In a neuroimaging study published in PLOS ...
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