2022/01/05 by Thomas L. Carroll
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Algorithm #Artificial intelligence #Artificial neural network #Computation #Computer hardware #Computer memory #Computer network #Computer science #Construct (python library) #Fading #Neural Networks and Reservoir Computing #Neural dynamics and brain function #Reservoir computing #Semiconductor memory #Set (abstract data type) #cs.NE
paper · pdf · doi:10.1063/5.0078151
arxiv created 2022/01/05 · openalex publication_date 2022/02/01 · arxiv updated 2022/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A reservoir computer is a way of using a high dimensional dynamical system for computation. One way to construct a reservoir computer is by connecting a set of nonlinear nodes into a network. Because the network creates feedback between nodes, the reservoir computer has memory. If the reservoir computer is to respond to an input signal in a consistent way (a necessary condition for computation), the memory must be fading; that is, the influence of the initial conditions fades over time. How long this memory lasts is important for determining how well the reservoir computer can solve a particular problem. In this paper I describe ways to vary the length of the fading memory in reservoir computers. Tuning the memory can be important to achieve optimal results in some problems; too much or too little memory degrades the accuracy of the computation.