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Research Topic Ideas in Reservoir Computing

Research Topic Ideas in Reservoir Computing

  Reservoir computing (RC) is the superior machine learning algorithm for data processing generated by dynamical systems utilizing the observed time-series data. RC leads the learning algorithm faster through transforming sequential input non-linearly into high dimensional space, which is efficiently read out by the learning algorithm. The significance of RC is better in processing both temporal and sequential data and reduces the effective computational cost. RC comprises several recurrent neural networks and provides fast learning with low training costs.

  A few types of RC are Context reverberation network, Echo statenet work, Backpropagation decorrelation, Liquid-state machine, Nonlinear Transient Computation, and Deep reservoir computing. Application fields of reservoir computing are biomedical, visual, audio, machinery, engineering, communication, environmental, financial, social, and security. Some of the application tasks of reservoir computing are pattern classification, time-series forecasting, pattern generation, adaptive filtering and control, system approximation, and short-term memory. Recent trends in RC are spiking dynamics reservoir computing, reservoir computing on structured data, time delay reservoir computing, and many more.

  • Reservoir computing (RC) is neuromorphic computing inspired by the human brain that allows harnessing the dynamics of a reservoir to perform temporal or sequential data processing.

  • Reservoir computing networks play a vital role in universal dynamical systems because of capable of learning the dynamics of other systems.

  • Due to the inherent flexibility of implementation, recently, RC has grown in these areas involving neuroscience and cognitive science, machine learning, and unconventional computing.

  • RC facilitates solving multiple tasks in parallel, resulting in high throughput. 

  • Even though RC algorithms helped bypass huge problems in machine learning, it has some shortcomings, such as that the models will not perform well without sufficient large training sets. Also, computation and memory are inseparable and hard to analyze in RC.