The human brain consists of 100 billion neurons connected to each other with synapses, with more than 10 thousand of them per neuron. When a neuron reaches a threshold voltage, due to the summation of currents flowing in from synapses or by external stimulation, the neuron fires with an action potential, observed as a voltage spike. It is this nonlinear firing behavior together with firing rate that gives rise to synaptic plasticity and forms the basis of memory, perception, action, and behavior. A perturbed firing of the underlying neuron can therefore evolve into undesirable mental states linked to various neurological disorders. Closed-loop control of the firing rate of the neuron is thus desirable, but very challenging due to the underlying highly nonlinear dynamics. A Hodgkin-Huxley model of the nonlinear neural dynamics in terms of its membrane potential with respect to current stimulation is developed together with its underlying ion channel dynamics in terms of gating variables. The model is linearized at operating point and lead, lag and lead-lag compensators are synthesized. Next, a dynamic inversion nonlinear controller, a robust incremental dynamic inversion controller, and a model predictive controller are derived to directly regulate the nonlinear neuron dynamics in terms of controlled firing. The synthesis of lead, lag, lead-lag, dynamic inversion, model predictive control-based feedback controllers over the nonlinear dynamics of the neuron led to controlled firing and a fast response that was robust to stochastic synaptic noise.