Reservoir Computing

Reservoir Computing is a type of neural network that processes information by using a "reservoir" of interconnected neurons, which are often randomly connected. Unlike traditional neural networks that require extensive training, Reservoir Computing leverages the dynamic behavior of the reservoir to transform input data into complex patterns. Only the output layer is trained, making it efficient and quicker to set up. This approach is effective for tasks like time-series prediction and signal processing, especially in cases where capturing temporal information is crucial. Its simplicity and effectiveness make it a popular choice in the field of machine learning.