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

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 in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight

Relating a calcium indicator signal to the unperturbed calcium concentration time-course

BACKGROUND: Optical indicators of cytosolic calcium levels have become important experimental tools in systems and cellular neuroscience. Indicators are known to interfere with intracellular calcium levels by acting as additional buffers, and this may strongly alter the time-course of various dynamical variables to be measured. RESULTS: By investigating the underlying reaction kinetics, we show that in some ranges of kinetic parameters one can explicitly link the time dependent indicator signal to the time-course of the calcium influx, and thus, to the unperturbed calcium level had there been no indicator in the cell

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

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 in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight

An optimization-based approach to calculating neutrino flavor evolution

We assess the utility of an optimization-based data assimilation (D.A.) technique for treating the problem of nonlinear neutrino flavor transformation in core collapse supernovae. D.A. uses measurements obtained from a physical system to estimate the state variable evolution and parameter values of the associated model. Formulated as an optimization procedure, D.A. can offer an integration-blind approach to predicting model evolution, which offers an advantage for models that thwart solution via traditional numerical integration techniques. Further, D.A. performs most optimally for models whose equations of motion are nonlinearly coupled. In this exploratory work, we consider a simple steady-state model with two mono-energetic neutrino beams coherently interacting with each other and a background medium. As this model can be solved via numerical integration, we have an independent consistency check for D.A. solutions. We find that the procedure can capture key features of flavor evolution over the entire trajectory, even given measurements of neutrino flavor only at the endpoint, and with an assumed known initial flavor distribution. Further, the procedure permits an examination of the sensitivity of flavor evolution to estimates of unknown model parameters, locates degeneracies in parameter space, and can identify the specific measurements required to break those degeneracies.Comment: 26 pages, 4 figure