Dynamical estimation of neuron and network properties I: variational methods

Toth, Bryan; Kostuk, Mark; Meliza, C. Daniel; Margoliash, Daniel; Abarbanel, Henry D. I.

doi:10.1007/s00422-011-0459-1

Cited by 54 publications

(52 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, in our model description of ionic currents, we utilized functional forms used in prior published neural modeling studies of non-HVC neurons. We did not attempt to calibrate the shape parameters of these functions, which would best be done using a voltage-clamp protocol or by using dynamical estimation methods as in Toth et al (2011). Doing this calibration would improve the fit of the models.…”

Section: Discussionmentioning

confidence: 99%

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

Daou

Ross

Johnson

et al. 2013

Journal of Neurophysiology

View full text Add to dashboard Cite

The nucleus HVC (proper name) within the avian analog of mammal premotor cortex produces stereotyped instructions through the motor pathway leading to precise, learned vocalization by songbirds. Electrophysiological characterization of component HVC neurons is an important requirement in building a model to understand HVC function. The HVC contains three neural populations: neurons that project to the RA (robust nucleus of arcopallium), neurons that project to Area X (of the avian basal ganglia), and interneurons. These three populations are interconnected with specific patterns of excitatory and inhibitory connectivity, and they fire with characteristic patterns both in vivo and in vitro. We performed whole cell current-clamp recordings on HVC neurons within brain slices to examine their intrinsic firing properties and determine which ionic currents are responsible for their characteristic firing patterns. We also developed conductance-based models for the different neurons and calibrated the models using data from our brain slice work. These models were then used to generate predictions about the makeup of the ionic currents that are responsible for the different responses to stimuli. These predictions were then tested and verified in the slice using pharmacological manipulations. The model and the slice work highlight roles of a hyperpolarization-activated inward current (Ih), a low-threshold T-type Ca(2+) current (ICa-T), an A-type K(+) current (IA), a Ca(2+)-activated K(+) current (ISK), and a Na(+)-dependent K(+) current (IKNa) in driving the characteristic neural patterns observed in the three HVC neuronal populations. The result is an improved characterization of the HVC neurons responsible for song production in the songbird.

show abstract

Section: Discussionmentioning

confidence: 99%

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

Daou

Ross

Johnson

et al. 2013

Journal of Neurophysiology

View full text Add to dashboard Cite

show abstract

“…We linearized Eq. 2 at each node t i using Simpson’s method35 to obtain L ( N + 1) equality constraints. A second set of constraints was specified by setting the minimum and maximum boundaries of the parameter search interval: p L ≤ p ≤ p U .…”

Section: Resultsmentioning

confidence: 99%

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

Nogaret

Meliza

Margoliash

2016

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Since the introduction of the UKF to neuronal dynamics by Voss et al in 2004 [11], a few investigators have applied these methods to the study of biological systems [43]–[48]. Other data assimilation techniques have also been successfully applied to study neuronal dynamics [49]. Nevertheless, the sleep modeling community has yet to utilize these resources.…”

Section: Discussionmentioning

confidence: 99%

Reconstructing Mammalian Sleep Dynamics with Data Assimilation

Sedigh-Sarvestani

Gluckman

2012

PLoS Comput Biol

View full text Add to dashboard Cite

Data assimilation is a valuable tool in the study of any complex system, where measurements are incomplete, uncertain, or both. It enables the user to take advantage of all available information including experimental measurements and short-term model forecasts of a system. Although data assimilation has been used to study other biological systems, the study of the sleep-wake regulatory network has yet to benefit from this toolset. We present a data assimilation framework based on the unscented Kalman filter (UKF) for combining sparse measurements together with a relatively high-dimensional nonlinear computational model to estimate the state of a model of the sleep-wake regulatory system. We demonstrate with simulation studies that a few noisy variables can be used to accurately reconstruct the remaining hidden variables. We introduce a metric for ranking relative partial observability of computational models, within the UKF framework, that allows us to choose the optimal variables for measurement and also provides a methodology for optimizing framework parameters such as UKF covariance inflation. In addition, we demonstrate a parameter estimation method that allows us to track non-stationary model parameters and accommodate slow dynamics not included in the UKF filter model. Finally, we show that we can even use observed discretized sleep-state, which is not one of the model variables, to reconstruct model state and estimate unknown parameters. Sleep is implicated in many neurological disorders from epilepsy to schizophrenia, but simultaneous observation of the many brain components that regulate this behavior is difficult. We anticipate that this data assimilation framework will enable better understanding of the detailed interactions governing sleep and wake behavior and provide for better, more targeted, therapies.

show abstract

Dynamical estimation of neuron and network properties I: variational methods

Cited by 54 publications

References 14 publications

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

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

Reconstructing Mammalian Sleep Dynamics with Data Assimilation

Contact Info

Product

Resources

About