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Objective:  Using data-driven methods to design stimuli (e.g., electrical currents) which evoke desired neural responses in different neuron-types for applications in treating neural disorders.  Methods:  The problem of stimulus design is formulated as estimating the inverse of a many-to-one non-linear &#x201C;forward&#x201D; mapping, which takes as input the parameters of waveform and outputs the corresponding neural response, directly from the data. A novel optimization framework &#x201C;PATHFINDER&#x201D; is proposed in order to estimate the previously mentioned inverse mapping. A comparison with existing data-driven methods, namely conditional density estimation methods and numerical inversion of an estimated forward mapping is performed with different dataset sizes in toy examples and in detailed computational models of biological neurons.  Results:  Using data from toy examples, as well as computational models of biological neurons, we show that PATHFINDER can outperform existing methods when the number of samples is low (i.e., a few hundred).  Significance:  Traditionally, the design of such stimuli has been model-driven and/or uses simplistic intuition, often aided by trial-and-error. Due to the inherent challenges in accurately modeling neural responses, as well as the sophistication of stimuli&#x0027;s effect on neural membrane potentials, data-driven approaches offer an attractive alternative. Our results suggest that PATHFINDER can be applied for optimizing stimulation parameters in experiments and treatments of neural disorders due to it requiring low number of data points.

PATHFINDER: Designing Stimulus for Neuromodulation Through Data-Driven Inverse Estimation of Non-Linear Functions