IEEE Transactions on Neural Networks and Learning Systems

Reservoir computing (RC) is a machine learning framework for temporal (sequential) pattern recognition, which originates from specific types of recurrent neural network models including echo state networks and liquid state machines. An RC system consists of a dynamical reservoir for mapping inputs into a high-dimensional temporal representation space and a readout for pattern analysis from the high-dimensional signal. Since only the readout part is trained typically with a simple linear algorithm, the learning cost of RC is extremely low compared with that of other recurrent neural networks. Such machine learning methods capable of high-speed learning are particularly demanded for online and real-time information processing systems. Further potential capabilities arise while addressing problems characterized by limited amount of supervised data. Due to these advantages, application fields of RC have been rapidly expanding. Theoretical aspects of RC have also been intensively studied in this decade. In addition, physical RC which exploits physical phenomena to realize the function of a reservoir has attracted much attention toward development of energy efficient machine learning hardware. This special issue focuses on new challenges for fully exploiting the potential of RC in practical applications and realizing extremely efficient information processing hardware. We would like to solicit high-quality works which pursue extremely efficient RC systems and devices, as well as breakthrough approaches to the model design and analysis. The topics include, but are not limited to: