With enormous data being generated every day from countless applications and sensor networks, the need for efficient computing devices to process and make use of this data continues to grow and is projected to increase in the future. The idea of analog computing has been redeemed recently and is poised as the future computing paradigm in these applications due to its powerful computing power and lower energy consumption. This work presents a mechanism where MEMS devices are coupled together resulting in dynamic behaviors that qualitatively resemble that of the biologically-inspired Continuous-Time Recurrent Neural Networks (CTRNNs). Moreover, interesting oscillations behaviors and limit cycles attributed to various weight manipulations and excitation input levels have been observed.

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