Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

ABSTRACT: We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability

SUBMITTER: Nogaret A 

PROVIDER: S-EPMC5015021 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature