Parameter estimation in single-compartment neuron models using a synchronization-based method

We describe a method for estimating parameter values in single-compartment neuron models from a recorded voltage trace. The technique involves synchronization between the model and the experimental data and minimizing a cost function that depends on the goodness of this synchrony with a minimum for optimal parameter values. We test our method using a previously developed single-compartment interneuron model that includes noise to mimic experimental data. Direct computation of the cost function over ranges of parameter values suggests that it changes smoothly, allowing for downhill optimization routines that utilize the gradient. We implement one such optimization routine with several kinetic parameters set at incorrect values. The optimization is fast and consistently returns good values. We discuss the considerations necessary when applying this approach to noisy data. In conclusion, we believe that this approach will be a useful tool in developing neuronal models.