Transfer Function-Based Characterization of the Honey Bee Olfactory System: From Biology to Electronic Circuits

We exemplify an interdisciplinary approach wherein a mesoscopic-scale functional model of a biological system is derived from time-series recordings, yielding transfer functions that can be used to design analog electronic circuits. Namely, sensory processing in the honey bee, a universal model for studying olfaction, is considered. Existing studies have focused on its antennal lobe, wherein only the responses of its functional units, known as glomeruli, have been accessible. Here, high temporal resolution calcium imaging is deployed to track the dynamics of odor-evoked activity beyond this processing stage. The responses in the somata outside of the antennal lobe are recorded, showing for the first time how the glomerular signals are transformed before entering the higher brain centers. A transfer function approach is applied to capture as a gray box model the remarkably heterogeneous signal transformations between odor input and glomerular response, and between glomerular signals and somata activity. The somata are tentatively mapped to the glomeruli via Granger causality, while machine learning classification and clustering allow grouping common properties regarding response amplitudes and temporal profiles. The obtained low-order transfer functions display time- and frequency-domain input-output properties closely similar to the biological system. Because transfer functions have universal applicability, once they have been determined, it is readily possible to design corresponding analog electronic circuits, with possible future applications in sensor signal conditioning. To exemplify this, examples based on resistor-capacitor (RC) networks and operational amplifiers are physically built and confirmed to generate responses highly correlated to the initial biological recordings.

Automated summary

We demonstrate an interdisciplinary approach to derive a mesoscopic-scale functional model of a biological system, which can be used to design analog electronic circuits. We focus on the sensory processing in honey bees and use high temporal resolution calcium imaging to track the dynamics of odor-evoked activity. By applying a transfer function approach, we capture the signal transformations between odor input and glomerular response, and between glomerular signals and somata activity. Through Granger causality and machine learning techniques, we map somata to glomeruli and group responses based on common properties. The obtained low-order transfer functions closely resemble the biological system's input-output properties and can be used for designing corresponding analog electronic circuits.