10 research outputs found
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
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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
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Nonlinear statistical data assimilation for HVCRA neurons in the avian song system
With the goal of building a model of the HVC nucleus in the avian song system, we discuss in detail a model of HVC[Formula: see text] projection neurons comprised of a somatic compartment with fast Na[Formula: see text] and K[Formula: see text] currents and a dendritic compartment with slower Ca[Formula: see text] dynamics. We show this model qualitatively exhibits many observed electrophysiological behaviors. We then show in numerical procedures how one can design and analyze feasible laboratory experiments that allow the estimation of all of the many parameters and unmeasured dynamical variables, given observations of the somatic voltage [Formula: see text] alone. A key to this procedure is to initially estimate the slow dynamics associated with Ca, blocking the fast Na and K variations, and then with the Ca parameters fixed estimate the fast Na and K dynamics. This separation of time scales provides a numerically robust method for completing the full neuron model, and the efficacy of the method is tested by prediction when observations are complete. The simulation provides a framework for the slice preparation experiments and illustrates the use of data assimilation methods for the design of those experiments
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