Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …

The ability to store and manipulate information is a hallmark of computational systems.
Whereas computers are carefully engineered to represent and perform mathematical …

Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling
sequential and time series data. They are notoriously hard to train because their loss …

We perform a systematic study of the approximation properties and optimization dynamics of
recurrent neural networks (RNNs) when applied to learn input-output relationships in …

Highlights•Many cortical circuits can be viewed as computational dynamical systems.•A new
tool to help us understand cortical dynamics is the optimized recurrent neural network …

Neural networks need the right representations of input data to learn. Here we ask how
gradient-based learning shapes a fundamental property of representations in recurrent …

Circuits of biological neurons, such as in the functional parts of the brain can be modeled as
networks of coupled oscillators. Inspired by the ability of these systems to express a rich set …

Trained recurrent networks are powerful tools for modeling dynamic neural computations.
We present a target-based method for modifying the full connectivity matrix of a recurrent …

Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term
dependencies in sequential data, but are notoriously hard to train because the error …

Recurrent neural networks have gained widespread use in modeling sequential data.
Learning long-term dependencies using these models remains difficult though, due to …