  We construct neuron models from data by transferring information from an
observed time series to the state variables and parameters of Hodgkin-Huxley
models. When the learning period completes, the model will predict additional
observations and its parameters uniquely characterise the complement of ion
channels. However, the assimilation of biological data, as opposed to model
data, is complicated by the lack of knowledge of the true neuron equations.
Reliance on guessed conductance models is plagued with multi-valued parameter
solutions. Here, we report on the distributions of parameters and currents
predicted with intentionally erroneous models, over-specified models, and an
approximate model fitting hippocampal neuron data. We introduce a recursive
piecewise data assimilation (RPDA) algorithm that converges with near-perfect
reliability when the model is known. When the model is unknown, we show model
error introduces correlations between certain parameters. The ionic currents
reconstructed from these parameters are excellent predictors of true currents
and carry a higher degree of confidence, >95.5%, than underlying parameters,
>53%. Unexpressed ionic currents are correctly filtered out even in the
presence of mild model error. When the model is unknown, the covariance
eigenvalues of parameter estimates are found to be a good gauge of model error.
Our results suggest that biological information may be retrieved from data by
focussing on current estimates rather than parameters.
