A generalized activating function for rapid and accurate neural response prediction in spinal cord stimulation

We present the Generalized Activating Function (GAF), a biophysics-grounded framework that accelerates neural activation predictions by up to three orders of magnitude relative to NEURON simulations of multi-compartment cable models while maintaining high predictive accuracy ( R 2 = 0.99). By overcoming computational bottlenecks in patient-specific modeling, the GAF enables practical translation of computational optimization into neuromodulation therapies such as spinal cord stimulation (SCS). We verify the GAF’s physiological validity by demonstrating near-perfect agreement with multi-compartment simulations for activation thresholds and spike initiation location/timing across multiple pulse widths in an anatomically realistic spine model. We validate clinical relevance by reproducing a published SCS optimization study for lower-limb motor recovery, matching NEURON-based prediction accuracy while reducing computation time from hours to seconds. Finally, we exploit the GAF’s speed to explore previously intractable parameter spaces: Pareto optimization over pulse waveforms uncovers solutions with approximately seven-fold increased energy efficiency at matched activation, while optimization of multielectrode stimulation (16 currents) increases the functional selectivity index for right hip flexion from 52% (clinical benchmark) to 82% in under one minute. By eliminating computational bottlenecks and enabling gains in efficiency and selectivity, the GAF transforms model-driven optimization into a clinically viable tool for patient-specific neuromodulation.