  We propose an extended reservoir computer that shows the functional
differentiation of neurons. The reservoir computer is developed to enable
changing of the internal reservoir using evolutionary dynamics, and we call it
an evolutionary reservoir computer. To develop neuronal units to show
specificity, depending on the input information, the internal dynamics should
be controlled to produce contracting dynamics after expanding dynamics.
Expanding dynamics magnifies the difference of input information, while
contracting dynamics contributes to forming clusters of input information,
thereby producing multiple attractors. The simultaneous appearance of both
dynamics indicates the existence of chaos. In contrast, sequential appearance
of these dynamics during finite time intervals may induce functional
differentiations. In this paper, we show how specific neuronal units are
yielded in the evolutionary reservoir computer.
