Effective Stimuli for Constructing Reliable Neuron Models
Figure 1
From experimental data to acceptable conductance-based neuron model.
(a) Data is collected from voltage responses to a set of repeated intracellular current injections (steps, ramps, noise currents) recorded from single cells' somata. Two repetitions of a step current injection are shown. Two traces with fairly large differences were chosen to highlight the variability. (b) The voltage traces are characterized using a set of features (e.g. firing rate, height of action potentials). For each feature both the experimental mean and standard deviation (SD) are obtained from 15 repetitions of the same stimulus. (c) The generic form of a model to be constrained consists of a reconstructed morphology and an assumed set of membrane ion channels (including their kinetics but not their densities, gi). (d) A multiple objective, genetic algorithm-based process of stochastic optimization is applied in order to obtain values for gi that minimize the distance between the experimentally measured set of features and those of the model. The convergence of the average error is shown by the blue curves, one curve for each of three independent applications of the model constraining procedure (e) For the many possible solutions at the final iteration, a selection criterion of two experimental SDs in each feature is used for choosing a subset of solutions (sets of gi values); these are considered acceptable models. Shown are two out of the six features considered for step stimuli (see Materials and methods) (f) An example of the response of two different successful models to a step current input as in a. Two models with fairly large differences were chosen to highlight the variability. The reconstructed L5 pyramidal cell shown in c is used throughout Figures 1–6.