LAUSR

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.