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

View Article: PubMed Central - PubMed

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 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.

No MeSH data available.


Membrane voltage oscillations used to extract the model parameters of two HVC neurons (Epoch 0).The experimental voltage  (black line) was recorded under current clamp stimulation by current waveform  (blue line). N1 is a putative RA-projecting neuron (top panel) and N2 is a X-projecting neuron (bottom panel). The assimilation window used to extract the parameters spans the interval [0–1600 ms] for N1 and [190 ms–1090 ms] for N2. The membrane voltage solution of the constrained optimization problem is V *(t) (green line). The membrane voltage predicted by integrating the experimental current waveform is  (red line). Details of the oscillations of N2 are plotted in supplementary Figure S7.
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f2: Membrane voltage oscillations used to extract the model parameters of two HVC neurons (Epoch 0).The experimental voltage (black line) was recorded under current clamp stimulation by current waveform (blue line). N1 is a putative RA-projecting neuron (top panel) and N2 is a X-projecting neuron (bottom panel). The assimilation window used to extract the parameters spans the interval [0–1600 ms] for N1 and [190 ms–1090 ms] for N2. The membrane voltage solution of the constrained optimization problem is V *(t) (green line). The membrane voltage predicted by integrating the experimental current waveform is (red line). Details of the oscillations of N2 are plotted in supplementary Figure S7.

Mentions: Figure 2 shows the two reference epochs which we use to construct the completed models of two representative HVC neurons: a putative RA-projecting neuron (N1) and a putative X-projecting neuron (N2). The current protocol (blue line) induced the oscillations observed in the membrane voltage (black line). Data assimilation was performed over 1600 ms long time interval (N1) and 900 ms (N2) using N = 80,000 and N = 90,000 mesh points respectively. The mesh size T/N was chosen to sample potential spikes with ≈100 data points each. The width of the data assimilation window was chosen as a tradeoff between the need to incorporate a statistically meaningful number of spikes and the need to minimize numerical error that accrues when handling larger Jacobian and Hessian matrices. With a constant mesh size, the optimum number of data points was empirically found to be N ≈ 100,000.


Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data
Membrane voltage oscillations used to extract the model parameters of two HVC neurons (Epoch 0).The experimental voltage  (black line) was recorded under current clamp stimulation by current waveform  (blue line). N1 is a putative RA-projecting neuron (top panel) and N2 is a X-projecting neuron (bottom panel). The assimilation window used to extract the parameters spans the interval [0–1600 ms] for N1 and [190 ms–1090 ms] for N2. The membrane voltage solution of the constrained optimization problem is V *(t) (green line). The membrane voltage predicted by integrating the experimental current waveform is  (red line). Details of the oscillations of N2 are plotted in supplementary Figure S7.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5015021&req=5

f2: Membrane voltage oscillations used to extract the model parameters of two HVC neurons (Epoch 0).The experimental voltage (black line) was recorded under current clamp stimulation by current waveform (blue line). N1 is a putative RA-projecting neuron (top panel) and N2 is a X-projecting neuron (bottom panel). The assimilation window used to extract the parameters spans the interval [0–1600 ms] for N1 and [190 ms–1090 ms] for N2. The membrane voltage solution of the constrained optimization problem is V *(t) (green line). The membrane voltage predicted by integrating the experimental current waveform is (red line). Details of the oscillations of N2 are plotted in supplementary Figure S7.
Mentions: Figure 2 shows the two reference epochs which we use to construct the completed models of two representative HVC neurons: a putative RA-projecting neuron (N1) and a putative X-projecting neuron (N2). The current protocol (blue line) induced the oscillations observed in the membrane voltage (black line). Data assimilation was performed over 1600 ms long time interval (N1) and 900 ms (N2) using N = 80,000 and N = 90,000 mesh points respectively. The mesh size T/N was chosen to sample potential spikes with ≈100 data points each. The width of the data assimilation window was chosen as a tradeoff between the need to incorporate a statistically meaningful number of spikes and the need to minimize numerical error that accrues when handling larger Jacobian and Hessian matrices. With a constant mesh size, the optimum number of data points was empirically found to be N ≈ 100,000.

View Article: PubMed Central - PubMed

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 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.

No MeSH data available.