How can we characterize the dynamics of neural networks with recurrent ... Hopfield's work inspired a new generation of recurrent network models; one early example was a learning algorithm that ...
Two new neural network designs promise to make AI models more adaptable and efficient, potentially changing how artificial ...
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 ...
This important work substantially advances our understanding of episodic memory by proposing a biologically plausible mechanism through which hippocampal barcode activity enables efficient memory ...
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, ...
However, the neural networks—computer models inspired by the human brain—behind these ... Liboni et al, Image segmentation ...
A collaborative team of researchers from Carnegie Mellon University and the University of Pittsburgh designed a clever experiment using a brain-controlled interface to determine whether one-way ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results
Feedback