  Biologically inspired recurrent neural networks, such as reservoir computers
are of interest in designing spatio-temporal data processors from a hardware
point of view due to the simple learning scheme and deep connections to Kalman
filters. In this work we discuss using in-depth simulation studies a way to
construct hardware reservoir computers using an analog stochastic neuron cell
built from a low energy-barrier magnet based magnetic tunnel junction and a few
transistors. This allows us to implement a physical embodiment of the
mathematical model of reservoir computers. Compact implementation of reservoir
computers using such devices may enable building compact, energy-efficient
signal processors for standalone or in-situ machine cognition in edge devices.
