  Reservoir computers are a type of neuromorphic computer that may be built a
an analog system, potentially creating powerful computers that are small, light
and consume little power. Typically a reservoir computer is build by connecting
together a set of nonlinear nodes into a network; connecting the nonlinear
nodes may be difficult or expensive, however. This work shows how a reservoir
computer may be expanded by adding functions to its output. The particular
functions described here are linear filters, but other functions are possible.
The design and construction of linear filters is well known, and such filters
may be easily implemented in hardware such as field programmable gate arrays
(FPGA's). The effect of adding filters on the reservoir computer performance is
simulated for a signal fitting problem, a prediction problem and a signal
classification problem.
