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In recent years, numerous mathematical models have been proposed to emulate neuronal behavior. Electronic circuits that analogically integrate these equations or exhibit dynamics qualitatively similar to those of biological neurons are known as electronic neurons. The advantage of analog integration, as opposed to software-based computation on conventional computers, becomes particularly significant when real-time interaction with living beings is required or intended. Analog integration circumvents delays associated with operating systems or the execution of software, which is crucial for timely responses in real-time applications. Building upon previous work that resulted in the construction of a minimally complex electronic neuron capable of displaying diverse excitable dynamics, we now present how to couple these units in a realistic manner, forming the foundational elements of a circuit capable of acoustically selective responses. Furthermore, we suggest the potential for integrating such circuits with actual biological systems, opening pathways for direct communication between memristive hardware neural networks and living brains.