Nonlinear oscillators can often be used as physical reservoir computers, in which the oscillator's dynamics simultaneously performs computation and stores information. Typically, the dynamic states are multiplexed in time, and then machine learning is used to unlock this stored information into a usable form. This time multiplexing is used to create virtual nodes, which are often necessary to capture enough information to perform different tasks, but this multiplexing procedure requires a relatively high sampling rate. Adaptive oscillators, which are a subset of nonlinear oscillators, have plastic states that learn and store information through their dynamics in a human readable form, without the need for machine learning. Highlighting this ability, adaptive oscillators have been used as analog frequency analyzers, robotic controllers, and energy harvesters. Here, adaptive oscillators are considered as a physical reservoir computer without the cumbersome time multiplexing procedure. With this multiplex-free physical reservoir computer architecture, the fundamental logic gates can be simultaneously calculated through dynamics without modifying the base oscillator.