Two new neural network designs promise to make AI models more adaptable and efficient, potentially changing how artificial ...

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 ...

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, ...

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 ...

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 ...

Among the marvels of the human brain is its ability to generalize. We see an object, like a chair, and we know it's a chair, ...