Studying neurons from an energy efficiency perspective has produced results in the research literature. This paper presents a method that enables computation of low energy input current stimuli that are able to drive a reduced Hodgkin-Huxley neuron model to approximate a prescribed time-varying reference membrane voltage. An optimal control technique is used to discover an input current that optimally minimizes a user selected balance between the square of the input stimulus current (input current 'energy') and the difference between the reference voltage and the membrane voltage (tracking error) over a stimulation period. Selecting reference signals to be membrane voltages produced by the neuron model in response to common types of input currents i(t) enables a comparison between i(t) and the determined optimal current stimulus i*(t). The intent is not to modify neuron dynamics, but through comparison of i(t) and i*(t) provide insight into neuron dynamics. Simulation results for four different bifurcation types demonstrate that this method consistently finds lower energy stimulus currents i*(t) that are able to approximate membrane voltages as produced by higher energy input currents i(t) in this neuron model.

Optimization techniques have been applied to neuron models for a variety of purposes, including control of neuron firing rates and minimizing input stimulus current magnitudes. Optimal control is used to minimize a quantity of interest; often, the time or energy needed to complete an objective. Rather than attempting to control or modify neuron dynamics, this paper demonstrates that optimal control can be used to obtain an optimal input stimulus current i*(t) which causes a six dimensional Hodgkin–Huxley type neuron model to approximate a specified reference membrane voltage. The reference voltages considered in this paper consist of one or more action potentials as evoked by an input current i(t). In the described method, the user prescribes a balance of low squared integral of input stimulus current (input stimulus “energy”) and accurate tracking of the original reference voltage. In a previous work, the authors applied this approach to a reduced order neuron model. This paper demonstrates the applicability of this technique to biologically plausible higher dimensional conductance based neuron models. For each investigated neuron response, the method discovered optimal input stimuli current i*(t) having a lower energy than the original i(t), while still providing accurate tracking of the reference voltage.