Publications (238)792.87 Total impact

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ABSTRACT: Information in measurements of a nonlinear dynamical system can be transferred to a quantitative model of the observed system to establish its fixed parameters and unobserved state variables. After this learning period is complete, one may predict the model response to new forces and, when successful, these predictions will match additional observations. This adjustment process encounters problems when the model is nonlinear and chaotic because dynamical instability impedes the transfer of information from the data to the model when the number of measurements at each observation time is insufficient. We discuss the use of information in the waveform of the data, realized through a time delayed collection of measurements, to provide additional stability and accuracy to this search procedure. Several examples are explored, including a few familiar nonlinear dynamical systems and small networks of Colpitts oscillators. 
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ABSTRACT: Cardiac rhythm management devices provide therapies for both arrhythmias and resynchronization but not heart failure, which affects millions of patients worldwide. This paper reviews recent advances in biophysics and mathematical engineering that provide a novel technological platform for addressing heart disease and enabling beattobeat adaptation of cardiac pacing in response to physiological feedback. The technology consists of silicon hardware central pattern generators (hCPG) that may be trained to emulate accurately the dynamical response of biological central pattern generators (bCPG). We discuss the limitations of present CPGs and appraise the advantages of analogue over digital circuits for application in bioelectronic medicine. To test the system, we have focused on the cardiorespiratory oscillators in the medulla oblongata that modulate heart rate in phase with respiration to induce respiratory sinus arrhythmia (RSA). We describe here a novel, scalable hCPG comprising physiologically realistic (HodgkinHuxley type) neurones and synapses. Our hCPG comprises two neurones that antagonise each other to provide rhythmic motor drive to the vagus nerve to slow the heart. We show how recent advances in modelling allow the motor output to adapt to physiological feedback such as respiration. In rats, we report on the restoration of RSA using an hCPG that receives diaphragmatic electromyography input and use it to stimulate the vagus nerve at specific time points of the respiratory cycle to slow the heart rate. We have validated the adaptation of stimulation to alterations in respiratory rate. We demonstrate that the hCPG is tuneable in terms of the depth and timing of the RSA relative to respiratory phase. These pioneering studies will now permit an analysis of the physiological role of RSA as well as its any potential therapeutic use in cardiac disease.This article is protected by copyright. All rights reservedThe Journal of Physiology 11/2014; 593(4). DOI:10.1113/jphysiol.2014.282723 · 4.54 Impact Factor 
Article: Estimating parameters and predicting membrane voltages with conductancebased neuron models.
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ABSTRACT: Recent results demonstrate techniques for fully quantitative, statistical inference of the dynamics of individual neurons under the HodgkinHuxley framework of voltagegated conductances. Using a variational approximation, this approach has been successfully applied to simulated data from model neurons. Here, we use this method to analyze a population of real neurons recorded in a slice preparation of the zebra finch forebrain nucleus HVC. Our results demonstrate that using only 1,500 ms of voltage recorded while injecting a complex current waveform, we can estimate the values of 12 state variables and 72 parameters in a dynamical model, such that the model accurately predicts the responses of the neuron to novel injected currents. A less complex model produced consistently worse predictions, indicating that the additional currents contribute significantly to the dynamics of these neurons. Preliminary results indicate some differences in the channel complement of the models for different classes of HVC neurons, which accords with expectations from the biology. Whereas the model for each cell is incomplete (representing only the somatic compartment, and likely to be missing classes of channels that the real neurons possess), our approach opens the possibility to investigate in modeling the plausibility of additional classes of channels the cell might possess, thus improving the models over time. These results provide an important foundational basis for building biologically realistic network models, such as the one in HVC that contributes to the process of song production and developmental vocal learning in songbirds.Biological Cybernetics 06/2014; 108(4). DOI:10.1007/s0042201406155 · 1.93 Impact Factor 
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ABSTRACT: We investigate the dynamics of a conductancebased neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.Physical Review E 06/2014; 89(61):062714. DOI:10.1103/PhysRevE.89.062714 · 2.33 Impact Factor 
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ABSTRACT: Utilizing the information in observations of a complex system to make accurate predictions through a quantitative model when observations are completed at time $T$, requires an accurate estimate of the full state of the model at time $T$. When the number of measurements $L$ at each observation time within the observation window is larger than a sufficient minimum value $L_s$, the impediments in the estimation procedure are removed. As the number of available observations is typically such that $L \ll L_s$, additional information from the observations must be presented to the model. We show how, using the time delays of the measurements at each observation time, one can augment the information transferred from the data to the model, removing the impediments to accurate estimation and permitting dependable prediction. We do this in a core geophysical fluid dynamics model, the shallow water equations, at the heart of numerical weather prediction. The method is quite general, however, and can be utilized in the analysis of a broad spectrum of complex systems where measurements are sparse. When the model of the complex system has errors, the method still enables accurate estimation of the state of the model and thus evaluation of the model errors in a manner separated from uncertainties in the data assimilation procedure. 
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ABSTRACT: Utilizing the information in observations of a complex system to make accurate predictions through a quantitative model when observations are completed at time $T$, requires an accurate estimate of the full state of the model at time $T$. When the number of measurements $L$ at each observation time within the observation window is larger than a sufficient minimum value $L_s$, the impediments in the estimation procedure are removed. As the number of available observations is typically such that $L \ll L_s$, additional information from the observations must be presented to the model. We show how, using the time delays of the measurements at each observation time, one can augment the information transferred from the data to the model, removing the impediments to accurate estimation and permitting dependable prediction. We do this in a core geophysical fluid dynamics model, the shallow water equations, at the heart of numerical weather prediction. The method is quite general, however, and can be utilized in the analysis of a broad spectrum of complex systems where measurements are sparse. When the model of the complex system has errors, the method still enables accurate estimation of the state of the model and thus evaluation of the model errors in a manner separated from uncertainties in the data assimilation procedure. 
Article: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times
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ABSTRACT: Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.Biological Cybernetics 04/2014; 108(3). DOI:10.1007/s004220140601y · 1.93 Impact Factor 
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ABSTRACT: Transferring information from observations to models of complex systems may meet impediments when the number of observations at any observation time is not sufficient. This is especially so when chaotic behavior is expressed. We show how to use timedelay embedding, familiar from nonlinear dynamics, to provide the information required to obtain accurate state and parameter estimates. Good estimates of parameters and unobserved states are necessary for good predictions of the future state of a model system. This method may be critical in allowing the understanding of prediction in complex systems as varied as nervous systems and weather prediction where insufficient measurements are typical.Physics Letters A 02/2014; 378(1112). DOI:10.1016/j.physleta.2014.01.027 · 1.63 Impact Factor 
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ABSTRACT: The authors consider statistical ensemble data assimilation for a onelayer shallowwater equation in a twin experiment: data are generated by an N x N enstrophyconserving grid integration scheme along with an Ekman vertical velocity at the bottom of an Ekman layer driving the flow and Rayleigh and eddy viscosity dissipation damping the flow. Data are generated for N = 16 and the chaotic flow that results is analyzed. This analysis is performed in a pathintegral formulation of the data assimilation problem. These path integrals are estimated by a Monte Carlo method using a Metropolis Hastings algorithm. The authors' concentration is on the number of measurements Lc that must be assimilated by the model to allow accurate estimation of the full state of the model at the end of an observation window. It is found that for this shallowwater flow approximately 70% of the full set of state variables must be observed to accomplish either goal. The number of required observations is determined by examining the number needed to synchronize the observed data Lc and the model output when L data streams are assimilated by the model. Synchronization occurs when L >= Lc and the correct selection of which Lc data are observed is made. If the number of observations is too small, so synchronization does not occur, or the selection of observations does not lead to synchronization of the data with the model output, state estimates during and at the end of the observation window and predictions beyond the observation window are inaccurate.Monthly Weather Review 07/2013; 141(7):25022518. DOI:10.1175/MWRD1200103.1 · 3.62 Impact Factor 
Article: Dynamical estimation of neuron and network properties II: Path integral Monte Carlo methods.
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ABSTRACT: HodgkinHuxley (HH) models of neuronal membrane dynamics consist of a set of nonlinear differential equations that describe the timevarying conductance of various ion channels. Using observations of voltage alone we show how to estimate the unknown parameters and unobserved state variables of an HH model in the expected circumstance that the measurements are noisy, the model has errors, and the state of the neuron is not known when observations commence. The joint probability distribution of the observed membrane voltage and the unobserved state variables and parameters of these models is a path integral through the model state space. The solution to this integral allows estimation of the parameters and thus a characterization of many biological properties of interest, including channel complement and density, that give rise to a neuron's electrophysiological behavior. This paper describes a method for directly evaluating the path integral using a Monte Carlo numerical approach. This provides estimates not only of the expected values of model parameters but also of their posterior uncertainty. Using test data simulated from neuronal models comprising several common channels, we show that short (<50 ms) intracellular recordings from neurons stimulated with a complex timevarying current yield accurate and precise estimates of the model parameters as well as accurate predictions of the future behavior of the neuron. We also show that this method is robust to errors in model specification, supporting model development for biological preparations in which the channel expression and other biophysical properties of the neurons are not fully known.Biological Cybernetics 04/2012; 106(3):15567. DOI:10.1007/s0042201204875 · 1.93 Impact Factor 
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ABSTRACT: Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spikebased very largescale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for modelbased identification and configuration of neuromorphic multichip VLSI systems.Neural Computation 03/2012; 24(7):166994. DOI:10.1162/NECO_a_00293 · 1.69 Impact Factor 
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ABSTRACT: We present a method for using measurements of membrane voltage in individual neurons to estimate the parameters and states of the voltagegated ion channels underlying the dynamics of the neuron's behavior. Short injections of a complex timevarying current provide sufficient data to determine the reversal potentials, maximal conductances, and kinetic parameters of a diverse range of channels, representing tens of unknown parameters and many gating variables in a model of the neuron's behavior. These estimates are used to predict the response of the model at times beyond the observation window. This method of [Formula: see text] extends to the general problem of determining model parameters and unobserved state variables from a sparse set of observations, and may be applicable to networks of neurons. We describe an exact formulation of the tasks in nonlinear data assimilation when one has noisy data, errors in the models, and incomplete information about the state of the system when observations commence. This is a high dimensional integral along the path of the model state through the observation window. In this article, a stationary path approximation to this integral, using a variational method, is described and tested employing data generated using neuronal models comprising several common channels with HodgkinHuxley dynamics. These numerical experiments reveal a number of practical considerations in designing stimulus currents and in determining model consistency. The tools explored here are computationally efficient and have paths to parallelization that should allow large individual neuron and network problems to be addressed.Biological Cybernetics 10/2011; 105(34):21737. DOI:10.1007/s0042201104591 · 1.93 Impact Factor 
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ABSTRACT: The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a Graphics Processing Unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a HodgkinHuxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 300, compared to an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.Journal of Computational Physics 03/2011; 230(22). DOI:10.1016/j.jcp.2011.07.015 · 2.49 Impact Factor 
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ABSTRACT: In using data assimilation to import information from observations to estimate parameters and state variables of a model, one must assume a distribution for the noise in the measurements and in the model errors. Using the path integral formulation of data assimilation~ cite{abar2009}, we introduce the idea of self consistency of the distribution of stochastic model errors: the distribution of model errors from the path integral with observed data should be consistent with the assumption made in formulating the the path integral. The path integral setting for data assimilation is discussed to provide the setting for the consistency test. Using two examples drawn from the 1996 Lorenz model, for $D = 100$ and for $D = 20$ we show how one can test for this inconsistency with essential no additional effort than that expended in extracting answers to interesting questions from data assimilation itself. \end{abstract} 
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ABSTRACT: The process of transferring information from observations of a dynamical system to estimate the fixed parameters and unobserved states of a system model can be formulated as the evaluation of a discretetime path integral in model state space. The observations serve as a guiding ‘potential’ working with the dynamical rules of the model to direct system orbits in state space. The pathintegral representation permits direct numerical evaluation of the conditional mean path through the state space as well as conditional moments about this mean. Using a Monte Carlo method for selecting paths through state space, we show how these moments can be evaluated and demonstrate in an interesting model system the explicit influence of the role of transfer of information from the observations. We address the question of how many observations are required to estimate the unobserved state variables, and we examine the assumptions of Gaussianity of the underlying conditional probability. Copyright © 2010 Royal Meteorological SocietyQuarterly Journal of the Royal Meteorological Society 10/2010; 136(652):1855  1867. DOI:10.1002/qj.690 · 5.13 Impact Factor 
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ABSTRACT: Throughout the brain, neurons encode information in fundamental units of spikes. Each spike represents the combined thresholding of synaptic inputs and intrinsic neuronal dynamics. Here, we address a basic question of spike train formation: how do perithreshold synaptic inputs perturb the output of a spiking neuron? We recorded from single entorhinal principal cells in vitro and drove them to spike steadily at ∼5 Hz (theta range) with direct current injection, then used a dynamicclamp to superimpose strong excitatory conductance inputs at varying rates. Neurons spiked most reliably when the input rate matched the intrinsic neuronal firing rate. We also found a striking tendency of neurons to preserve their rates and coefficients of variation, independently of input rates. As mechanisms for this rate maintenance, we show that the efficacy of the conductance inputs varied with the relationship of input rate to neuronal firing rate, and with the arrival time of the input within the natural period. Using a novel method of spike classification, we developed a minimal Markov model that reproduced the measured statistics of the output spike trains and thus allowed us to identify and compare contributions to the rate maintenance and resonance. We suggest that the strength of rate maintenance may be used as a new categorization scheme for neuronal response and note that individual intrinsic spiking mechanisms may play a significant role in forming the rhythmic spike trains of activated neurons; in the entorhinal cortex, individual pacemakers may dominate production of the regional theta rhythm.European Journal of Neuroscience 10/2010; 32(11):19309. DOI:10.1111/j.14609568.2010.07455.x · 3.67 Impact Factor 
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ABSTRACT: In variational formulations of data assimilation, the estimation of parameters or initial state values by a search for a minimum of a cost function can be hindered by the numerous local minima in the dependence of the cost function on those quantities. We argue that this is a result of instability on the synchronization manifold where the observations are required to match the model outputs in the situation where the data and the model are chaotic. The solution to this impediment to estimation is given as controls moving the positive conditional Lyapunov exponents on the synchronization manifold to negative values and adding to the cost function a penalty that drives those controls to zero as a result of the optimization process implementing the assimilation. This is seen as the solution to the proper size of ‘nudging’ terms: they are zero once the estimation has been completed, leaving only the physics of the problem to govern forecasts after the assimilation window.We show how this procedure, called Dynamical State and Parameter Estimation (DSPE), works in the case of the Lorenz96 model with nine dynamical variables. Using DSPE, we are able to accurately estimate the fixed parameter of this model and all of the state variables, observed and unobserved, over an assimilation time interval [0, T]. Using the state variables at T and the estimated fixed parameter, we are able to accurately forecast the state of the model for t > T to those times where the chaotic behaviour of the system interferes with forecast accuracy. Copyright © 2010 Royal Meteorological SocietyQuarterly Journal of the Royal Meteorological Society 01/2010; 136(648):769  783. DOI:10.1002/qj.600 · 5.13 Impact Factor 
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ABSTRACT: Data assimilation is a problem in estimating the fixed parameters and state of a model of an observed dynamical system as it receives inputs from measurements passing information to the model. Using methods developed in statistical physics, we present effective actions and equations of motion for the mean orbits associated with the temporal development of a dynamical model when it has errors, there is uncertainty in its initial state, and it receives information from noisy measurements. If there are statistical dependences among errors in the measurements they can be included in this approach.Physics Letters A 10/2009; 373(44):40444048. DOI:10.1016/j.physleta.2009.08.072 · 1.63 Impact Factor 
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ABSTRACT: Ensemble data assimilation is a problem in determining the most likely phase space trajectory of a model of an observed dynamical sys tem as it receives inputs from measurements passing information to the model. Using methods developed in statistical physics, we present effective actions and equations of motion for the mean orbits associ ated with the temporal development of a dynamical model when it has errors, there is uncertainty in its initial state, and it receives informa tion from measurements. If there are correlations among errors in the measurements they are naturally included in this approach. Comment: 10 pages 
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ABSTRACT: We examine the use of synchronization as a mechanism for extracting parameter and state information from experimental systems. We focus on important aspects of this problem that have received little attention previously and we explore them using experiments and simulations with the chaotic Colpitts oscillator as an example system. We explore the impact of model imperfection on the ability to extract valid information from an experimental system. We compare two optimization methods: an initial value method and a constrained method. Each of these involves coupling the model equations to the experimental data in order to regularize the chaotic motions on the synchronization manifold. We explore both timedependent and timeindependent coupling and discuss the use of periodic impulse coupling. We also examine both optimized and fixed (or manually adjusted) coupling. For the case of an optimized timedependent coupling function u(t) we find a robust structure which includes sharp peaks and intervals where it is zero. This structure shows a strong correlation with the location in phase space and appears to depend on noise, imperfections of the model, and the Lyapunov direction vectors. For timeindependent coupling we find the counterintuitive result that often the optimal rms error in fitting the model to the data initially increases with coupling strength. Comparison of this result with that obtained using simulated data may provide one measure of model imperfection. The constrained method with timedependent coupling appears to have benefits in synchronizing long data sets with minimal impact, while the initial value method with timeindependent coupling tends to be substantially faster, more flexible, and easier to use. We also describe a method of coupling which is useful for sparse experimental data sets. Our use of the Colpitts oscillator allows us to explore in detail the case of a system with one positive Lyapunov exponent. The methods we explored are easily extended to driven systems such as neurons with timedependent injected current. They are expected to be of value in nonchaotic systems as well. Software is available on request.Physical Review E 08/2009; 80(1 Pt 2):016201. DOI:10.1103/PhysRevE.80.016201 · 2.33 Impact Factor
Publication Stats
11k  Citations  
792.87  Total Impact Points  
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Institutions

1970–2014

University of California, San Diego
 • Department of Physics
 • Marine Physical Laboratory (MPL)
 • Institute for Nonlinear Science (INLS)
San Diego, California, United States


2012

ETH Zurich
 Institute of Neuroinformatics
Zürich, ZH, Switzerland


2008

The Scripps Research Institute
La Jolla, California, United States 
ChristianAlbrechtsUniversität zu Kiel
 Institute for Theoretical Physics and Astrophysics (ITAP)
Kiel, SchleswigHolstein, Germany


1987–2008

National University (California)
San Diego, California, United States


2005

Freie Universität Berlin
 Institute of Biology
Berlin, Land Berlin, Germany


1992–2003

CSU Mentor
Long Beach, California, United States


1996

Georgia Institute of Technology
Atlanta, Georgia, United States 
University of California, Santa Barbara
 Department of Physics
Santa Barbara, California, United States


1975–1983

University of California, Berkeley
 • Lawrence Berkeley Laboratory
 • Department of Physics
Berkeley, California, United States


1975–1978

Fermi National Accelerator Laboratory (Fermilab)
Батавия, Illinois, United States


1976

Stanford University
Palo Alto, California, United States


1974

California Institute of Technology
Pasadena, California, United States


1972

Weizmann Institute of Science
Israel


1966–1972

Princeton University
 Department of Physics
Princeton, New Jersey, United States
