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 ...

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

Gated Recurrent Unit (GRU) is a type of Recurrent Neural Network (RNN) which performs better than Simple RNN while dealing with longer input data. Gated Recurrent Unit (GRU) is an advance RNN which ...