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We construct neuron models from data by transferring information from anobserved time series to the state variables and parameters of Hodgkin-Huxleymodels. When the learning period completes, the model will predict additionalobservations and its parameters uniquely characterise the complement of ionchannels. However, the assimilation of biological data, as opposed to modeldata, is complicated by the lack of knowledge of the true neuron equations.Reliance on guessed conductance models is plagued with multi-valued parametersolutions. Here, we report on the distributions of parameters and currentspredicted with intentionally erroneous models, over-specified models, and anapproximate model fitting hippocampal neuron data. We introduce a recursivepiecewise data assimilation (RPDA) algorithm that converges with near-perfectreliability when the model is known. When the model is unknown, we show modelerror introduces correlations between certain parameters. The ionic currentsreconstructed from these parameters are excellent predictors of true currentsand carry a higher degree of confidence, >95.5%, than underlying parameters,>53%. Unexpressed ionic currents are correctly filtered out even in thepresence of mild model error. When the model is unknown, the covarianceeigenvalues of parameter estimates are found to be a good gauge of model error.Our results suggest that biological information may be retrieved from data byfocussing on current estimates rather than parameters.

Inferring the dynamics of ionic currents from recursive piecewise data assimilation of approximate neuron models