Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.

Reservoir computers (RCs) are powerful machine learning architectures for time series prediction. Recently, next generation reservoir computers (NGRCs) have been introduced, offering distinct advantages over RCs, such as reduced computational expense and lower training data requirements. However, NGRCs have their own practical difficulties, including sensitivity to sampling time and type of nonlinearities in the data.

Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.

Recent work has shown that machine learning (ML) models can skillfully forecast the dynamics of unknown chaotic systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics ("climate") can be produced by employing a feedback loop, whereby the model is trained to predict forward only one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this feedback can result in artificially rapid error growth ("instability").

The neurogenic bladder: developing a consensus bladder and bowel management pathway for people with MS.

This article outlines how the bladder can be affected in neurological conditions such as multiple sclerosis (MS) and the impact this has on patient quality of life and NHS resources. A group of MS and bladder and bowel nurse specialists has developed consensus bladder pathways in the hope that all nurses in contact with patients who are likely to have neurogenic bladder symptoms become 'bladder aware'.

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks.

Forecasting the dynamics of large, complex, sparse networks from previous time series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topology of the network of interest. We demonstrate the utility and scalability of our method implemented using reservoir computing on a chaotic network of oscillators.