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 ...

This work establishes a valuable theoretical finding about how the spike timing dependence of inhibitory plasticity shapes recurrent network connectivity. The combination of theoretical analysis and ...

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 ...

What Is A Recurrent Neural Network (RNN)? Recurrent Neural Networks (RNNs) are artificial neural networks designed to handle sequential data like text, speech or financial records. Unlike traditional ...

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

This paper puts forward a method using neural networks to minimize the Wasserstein distance between log signatures to reduce the target dimension of the ...