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Everything about Reservoir computing

Type of recurrent neural network with random and non-trainable internal structure

Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed. The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Reservoir computingA 5-minute orientation
  2. Skim a recent paper: An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computingInfluential work since 2022
  3. Pick up a classic book: Engineering and designThe most-reprinted book on the subject
  4. Read the landmark paper: Reservoir computing approaches to recurrent neural network trainingCited 2,689 times
  5. Go sideways: Echo state networkA closely related topic
  6. Go sideways: Liquid state machineA closely related topic

📖 Definitions

Noun A framework for computation derived from recurrent neural network theory, mapping input signals into higher-dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.

🗂️ The subject at a glance

  1. History
  2. Classical reservoir computing
  3. Quantum reservoir computing

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics