Modeling signal transduction pathways with biologically-inspired deep ESN for membrane voltage dynamics in C. elegans olfactory neurons

抄録

Modeling how signal transduction pathways shape stimulus-evoked membrane-voltage dynamics is essential for linking molecular perturbations to computation. Existing approaches are either costly biophysical models or black-box systems, obscuring pathway roles and preventing computational knockouts. We propose a modular reservoir architecture, the Sequential Multi-Output Echo State Network (SMO-ESN), which partitions the reservoir into serial modules and applies structured dropout to mask module outputs. Trained on experimental voltage recordings from C. elegans AWA neurons under step odor stimulation in wild-type and an egl-19 null mutant, SMO-ESN achieves lower NRMSE than a baseline ESN and reproduces mutant-like responses, highlighting interpretability.