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The network studied here is based on a standard model in physics, but itappears in various applications ranging from spintronics to neuroscience. Whenthe network is forced by an external signal common to all its elements, thereare shown to be two potential (gradient) functions: One for amplitudes and onefor phases. But the phase potential disappears when the forcing is removed. Thephase potential describes the distribution of in-phase/anti-phase oscillationsin the network, as well as resonances in the form of phase locking. A valley ina potential surface corresponds to memory that may be accessed by associativerecall. The two potentials derived here exhibit two different forms of memory:structural memory (time domain memory) that is sustained in the free problem,and evoked memory (frequency domain memory) that is sustained by the phasepotential, only appearing when the system is illuminated by common externalforcing. The common forcing organizes the network into those elements that arelocked to forcing frequencies and other elements that may form secludedsub-networks. The secluded networks may perform independent operations such aspattern recognition and logic computations. Various control methods for shapingthe network's outputs are demonstrated.

A Frequency-Phase Potential for a Forced STNO Network: an Example of Evoked Memory