Population-level neurocontrol has been advanced predominately through the miniaturization of hardware, such as MEMS-based electrodes. However, miniaturization alone may not be viable as a method for single-neuron resolution control within large ensembles, as it is typically infeasible to create electrode densities approaching 1:1 ratios with the neurons whose control is desired. That is, even advanced neural interfaces will likely remain underactuated, in that there will be fewer inputs (electrodes) within a given area than there are outputs (neurons). A complementary “software” approach could allow individual electrodes to independently control multiple neurons simultaneously, to improve performance beyond naïve hardware limits. An underactuated control schema, demonstrated in theoretical analysis and simulation (Ching & Ritt, 2013), uses stimulus strength-duration tradeoffs to activate a target neuron while leaving non-targets inactive. Here I experimentally test this schema in vivo, by independently controlling pairs of cortical neurons receiving common optogenetic input, in anesthetized mice. With this approach, neurons could be specifically and independently controlled following a short (~3 min) identification procedure. However, drift in neural responsiveness limited the performance over time. I developed an adaptive control procedure that fits stochastic Integrate and Fire (IAF) models to blocks of neural recordings, based on the deviation of expected from observed spiking, and selects optimal stimulation parameters from the updated models for subsequent blocks. I find the adaptive approach can maintain control over long time periods (>20 minutes) in about 30% of tested candidate neuron pairs. Because stimulation distorts the observation of neural activity, I further analyzed the influence of various forms of spike sorting corruption, and proposed methods to compensate for their effects on neural control systems. Overall, these results demonstrate the feasibility of underactuated neurocontrol for in vivo applications as a method for increasing the controllable population of high density neural interfaces.