Imagine hearing a familiar sound and expecting something to happen. Before the event arrives, the brain can predict what it ...

Researchers at the University of Tokyo show that a single recurrent spiking neural network can learn event identity, timing, ...

A recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential ...

Researchers in Hangzhou have developed MGCRN, a graph-based recurrent neural network that maintains high forecasting accuracy ...

A key objective of several neuroscience studies is to understand and model how the dynamics of distinct populations of neurons give rise to specific human and animal behaviors. Many existing methods ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

A brain-inspired spiking network jointly learned what event would occur, when it would occur, and its likelihood using local ...

This study bridges classical time-series econometrics with modern machine learning by establishing theoretical performance guarantees for recurrent neural networks (RNNs) applied to complex ...

Both the predictive power and the memory storage capability of an artificial neural network called a reservoir computer increase when time delays are added into how the network processes signals, ...