Electrical Compartmentalization in Neurons

The dendritic tree of neurons plays an important role in information processing in the brain. While it is thought that dendrites require independent subunits to perform most of their computations, it is still not understood how they compartmentalize into functional subunits. Here, we show how these subunits can be deduced from the properties of dendrites. We devised a formalism that links the dendritic arborization to an impedance-based tree graph and show how the topology of this graph reveals independent subunits. This analysis reveals that cooperativity between synapses decreases slowly with increasing electrical separation and thus that few independent subunits coexist. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of the subunits in a context-dependent manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features. Analysis of dendritic patch-clamp recording experiments confirmed our theoretical predictions.Peer reviewe

A modeling study of the history-dependence of conduction delay in unmyelinated axons

Conduction delay in an axon is the time required for an action potential to propagate between two positions. It is a function of the axon’s passive membrane properties, voltage-gated ion channels and the Na+/K+ pump, and can be substantially affected by neuromodulators. The conduction delay of action potential, generated by the pyloric dilator (PD) neuron unmyel i nated motor axon in the stomatogastric nervous system, shows significant variability with ongoing bursting or Poisson stimulation. When the axon is stimulated, the mean value (Dmean) and coefficient variation of conduction delay (CV-D) slowly increase with time (slow timescale effect), and the relationship between delay and instantaneous stimulus frequency (Fi nst) is non-monotonic (fast timescale effect). This dissertation investigates how the history-dependence of conduction delay is generated and the contributions of different ionic currents to conduction delay. This dissertation is comprised of three parts. In the first part, we build a biophysical model that includes several characterized ionic currents and the Na+/K+ pump in order to unmask the mechanisms underlying the history dependence of conduction delay. This model captures both the slow and fast timescale effects of conduction delay obtained from the realistic burst stimulation and Poisson stimulation at different mean frequencies. Additionally, the effects of a neuromodulator (dopamine) and a channel blocker (CsCl) on the history-dependence of conduction delay were also accurately captured by the biophysical model. Specifically, the Na+/K+ pump plays a critical role in the slow increase of Dmean and CV-D. At the fast timescale, the non-monotonic relationship between conduction delay and Finst is captured by the dynamical properties of INa. Furthermore, we systematically investigated the contributions of different ionic currents on conduction delay and spike shape parameters (i.e., duration, trough and peak voltages) with realistic burst stimulation protocols. Specifically, we found that only INa substantially affects the variability of conduction delay. Based on this observation, in the second part of the dissertation, we intended to use the dynamical parameters of INa to build an equation to accurately predict the variability of conduction delay. We found that conduction delay is mostly determined by the opening rate of the Na+ activation variable prior to the action potential (αm(VT)), and the closing rate of its inactivation variable at the peak (flh(VP)). Consequently, we developed an empirical equation for conduction delay in our model using multivariate linear regression of the Poisson stimulation data. The resulting equation accurately predicted the history-dependence of conduction delay on novel data. In our model data both αm and βh are almost linear functions of their respective voltage variables (VT and VP) in the voltage ranges observed. We, therefore, simplified our empirical equation and the new equation can also accurately predict the history dependence of conduction delayin the model. More importantly, it provides accurate predictions of conduction delay from experimental measurements of action potential voltage trajectories in the motor axon without need of computational modeling. In the third and final part of the dissertation, I will develop a decoding technique to investigate the functional relationship between conduction delay and the history activity in the PD axon. Using biological data obtained from representative experiments of the PD axon with Poisson stimulation, all the parameters in the decoding technique are determined after a routine optimization process. With these optimized parameters, the decoding model can accurately predict the conduction delay only from the stimulus time. A similar technique is developed and applied to explore and predict the voltage facilitation exposed by the cpv2-a muscle. These results show that conduction delay is affected by the short- and long-term history activity in the PD axon. The conductance-based biophysical model, the empirical equations and the decoding technique, which were developed in this dissertation, provide quantitative tools to explore the mechanisms of history-dependence of conduction delay, and predict conduction delay both in the model results and in the experimental measurements

Reaction-diffusion systems in and out of equilibrium - methods for simulation and inference

Reaction-diffusion methods allow treatment of mesoscopic dynamic phenomena of soft condensed matter especially in the context of cellular biology. Macromolecules such as proteins consist of thousands of atoms, in reaction-diffusion models their interaction is described by effective dynamics with much fewer degrees of freedom. Reaction-diffusion methods can be categorized by the spatial and temporal length-scales involved and the amount of molecules, e.g. classical reaction kinetics are macroscopic equations for fast diffusion and many molecules described by average concentrations. The focus of this work however is interacting-particle reaction-dynamics (iPRD), which operates on length scales of few nanometers and time scales of nanoseconds, where proteins can be represented by coarse-grained beads, that interact via effective potentials and undergo reactions upon encounter. In practice these systems are often studied using time-stepping computer simulations. Reactions in such iPRD simulations are discrete events which rapidly interchange beads, e.g. in the scheme A + B C the two interacting particles A and B will be replaced by a C complex and vice-versa. Such reactions in combination with the interaction potentials pose two practical problems: 1. To achieve a well defined state of equilibrium, it is of vital importance that the reaction transitions obey microscopic reversiblity (detailed balance). 2. The mean rate of a bimolecular association reaction changes when the particles interact via a pair-potential. In this work the first question is answered both theoretically and algorithmically. Theoretically by formulating the state of equilibrium for a closed iPRD system and the requirements for detailed balance. Algorithmically by implementing the detailed balance reaction scheme in a publicly available simulator ReaDDy~2 for iPRD systems. The second question is answered by deriving concrete formulae for the macroscopic reaction rate as a function of the intrinsic parameters for the Doi reaction model subject to pair interactions. Especially this work addresses two important scenarios: Reversible reactions in a closed container and irreversible bimolecular reactions in the diffusion-influenced regime. A characteristic of reactions occurring in cellular environments is that the number of species involved in a physiological response is very large. Unveiling the network of necessary reactions is a task that can be addressed by a data-driven approach. In particular, analyzing observation data of such processes can be used to learn the important governing dynamics. This work gives an overview of the inference of dynamical reactive systems for the different reaction-diffusion models. For the case of reaction kinetics a method called Reactive Sparse Identification of Nonlinear Dynamics (Reactive SINDy) is developed that allows to obtain a sparse reaction network out of candidate reactions from time-series observations of molecule concentrations

Spatial pattern recognition for crop-livestock systems using multispectral data

Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues

Automated proximal sensing for estimation of the bidirectional reflectance distribution function in a Mediterranean tree-grass ecosystem

Premio Extraordinario de Doctorado de la UAH en el año académico 2015-2016Los sistemas automáticos de proximal sensing permiten adquirir información espectral de las cubiertas terrestres elevada frecuencia temporal, que puede relacionarse con observaciones remotas o de otros tipos de sensores como los sistemas de eddy covariance. Si bien inicialmente los sistemas automáticos empleaban sensores multi-banda, en los últimos años se ha incrementado el uso de sensores hiperespectrales. Si bien estos sensores ofrecen información redundante y de alta resolución espectral, las mediciones están sujetas a múltiples fuentes de incertidumbre; tanto instrumentales (dependencias de la temperatura o el nivel de señal) como direccionales (dependencia de la geometría de observación e iluminación). Las dependencias instrumentales pueden ser minimizadas, por ejemplo, controlando la temperatura del instrumento o el nivel de señal registrado. En otros casos, es necesario parametrizar y emplear modelos para corregir los datos. En la presente tesis doctoral los capítulos 1 al 3 presentan la caracterización completa de un espectrómetro de campo instalado en un sistema automático. Los capítulos 1 y 2 analizan las fuentes de no linealidad en este instrumento, una de las cuales no había sido anteriormente descrita en este tipo de instrumentos. El tercer capítulo muestra el conjunto completo de modelos de corrección de los efectos instrumentales y la cadena de procesado correspondiente. Por otro lado, los sistemas automáticos se enfrentan a efectos direccionales ya que adquieren mediciones continuamente durante el ciclo solar diario y bajo cualquier condición de iluminación. Esto maximiza los rangos de los ángulos de iluminación y también de la fracción difusa de la irradiancia. Esta variabilidad de condiciones de iluminación, combinada con una variación de los ángulos de observación permite obtener la información necesaria para caracterizar las respuestas direccionales de la cubierta observada. Algunos sistemas automáticos multi-angulares ya han sido empleados para realizar esta caracterización mediante la estimación de la Función de Distribución de Reflectividad Bidireccional (BRDF) en ecosistemas homogéneos. Sin embargo, esto no se ha conseguido aún en áreas heterogéneas, como es el caso de los ecosistemas tree-grass o de sabana. Así mismo, los trabajos previos no han considerado los efectos de la radiación difusa en el estudio del BRDF. En el capítulo 4 proponemos una metodología que permite desmezclar y caracterizar simultáneamente la función de distribución de reflectividad hemisférica-direccional de las dos cubiertas de vegetación presentes en el ecosistema, pasto y arbolado. También se analizan los efectos de las diferentes características del método. Finalmente, los resultados se escalan y se comparan con productos globales de satélite como el producto BRDF de MODIS. La conclusión obtenida es que se requieren más esfuerzos en el desarrollo y caracterización de sensores hiperespectrales instalados en sistemas automáticos de campo. Estos sistemas deberían adoptar configuraciones multi-angulares de modo que puedan caracterizarse las respuestas direccionales. Para ello, será necesario considerar los efectos de la radiación difusa; y en algunos casos también la heterogeneidad de la escena

Automated proximal sensing for estimation of the bidirectional reflectance distribution function in a Mediterranean tree-grass ecosystem

Premio Extraordinario de Doctorado de la UAH en el año académico 2015-2016Los sistemas automáticos de proximal sensing permiten adquirir información espectral de las cubiertas terrestres elevada frecuencia temporal, que puede relacionarse con observaciones remotas o de otros tipos de sensores como los sistemas de eddy covariance. Si bien inicialmente los sistemas automáticos empleaban sensores multi-banda, en los últimos años se ha incrementado el uso de sensores hiperespectrales. Si bien estos sensores ofrecen información redundante y de alta resolución espectral, las mediciones están sujetas a múltiples fuentes de incertidumbre; tanto instrumentales (dependencias de la temperatura o el nivel de señal) como direccionales (dependencia de la geometría de observación e iluminación). Las dependencias instrumentales pueden ser minimizadas, por ejemplo, controlando la temperatura del instrumento o el nivel de señal registrado. En otros casos, es necesario parametrizar y emplear modelos para corregir los datos. En la presente tesis doctoral los capítulos 1 al 3 presentan la caracterización completa de un espectrómetro de campo instalado en un sistema automático. Los capítulos 1 y 2 analizan las fuentes de no linealidad en este instrumento, una de las cuales no había sido anteriormente descrita en este tipo de instrumentos. El tercer capítulo muestra el conjunto completo de modelos de corrección de los efectos instrumentales y la cadena de procesado correspondiente. Por otro lado, los sistemas automáticos se enfrentan a efectos direccionales ya que adquieren mediciones continuamente durante el ciclo solar diario y bajo cualquier condición de iluminación. Esto maximiza los rangos de los ángulos de iluminación y también de la fracción difusa de la irradiancia. Esta variabilidad de condiciones de iluminación, combinada con una variación de los ángulos de observación permite obtener la información necesaria para caracterizar las respuestas direccionales de la cubierta observada. Algunos sistemas automáticos multi-angulares ya han sido empleados para realizar esta caracterización mediante la estimación de la Función de Distribución de Reflectividad Bidireccional (BRDF) en ecosistemas homogéneos. Sin embargo, esto no se ha conseguido aún en áreas heterogéneas, como es el caso de los ecosistemas tree-grass o de sabana. Así mismo, los trabajos previos no han considerado los efectos de la radiación difusa en el estudio del BRDF. En el capítulo 4 proponemos una metodología que permite desmezclar y caracterizar simultáneamente la función de distribución de reflectividad hemisférica-direccional de las dos cubiertas de vegetación presentes en el ecosistema, pasto y arbolado. También se analizan los efectos de las diferentes características del método. Finalmente, los resultados se escalan y se comparan con productos globales de satélite como el producto BRDF de MODIS. La conclusión obtenida es que se requieren más esfuerzos en el desarrollo y caracterización de sensores hiperespectrales instalados en sistemas automáticos de campo. Estos sistemas deberían adoptar configuraciones multi-angulares de modo que puedan caracterizarse las respuestas direccionales. Para ello, será necesario considerar los efectos de la radiación difusa; y en algunos casos también la heterogeneidad de la escena

All-Atom Multiscale Computational Modeling Of Viral Dynamics

Thesis (Ph.D.) - Indiana University, Chemistry, 2009Viruses are composed of millions of atoms functioning on supra-nanometer length scales over timescales of milliseconds or greater. In contrast, individual atoms interact on scales of angstroms and femtoseconds. Thus they display dual microscopic/macroscopic characteristics involving processes that span across widely-separated time and length scales. To address this challenge, we introduced automatically generated collective modes and order parameters to capture viral large-scale low-frequency coherent motions. With an all-atom multiscale analysis (AMA) of the Liouville equation, a stochastic (Fokker-Planck or Smoluchowski) equation and equivalent Langevin equations are derived for the order parameters. They are shown to evolve on timescales much larger than the 10^(-14)-second timescale of fast atomistic vibrations and collisions. This justifies a novel multiscale Molecular Dynamics/Order Parameter eXtrapolation (MD/OPX) approach, which propagates viral atomistic and nanoscale dynamics simultaneously by solving the Langevin equations of order parameters implicitly without the need to construct thermal-average forces and friction/diffusion coefficients. In MD/OPX, a set of short replica MD runs with random atomic velocity initializations estimate the ensemble average rate of change in order parameters, extrapolation of which is then used to project the system over long time. The approach was implemented by using NAMD as the MD platform. Application of MD/OPX to cowpea chlorotic mottle virus (CCMV) capsid revealed that its swollen state undergoes significant energy-driven shrinkage in vacuum during 200ns simulation, while for the native state as solvated in a host medium at pH 7.0 and ionic strength I=0.2M, the N-terminal arms of capsid proteins are shown to be highly dynamic and their fast fluctuations trigger global expansion of the capsid. Viral structural transitions associated with both processes are symmetry-breaking involving local initiation and front propagation. MD/OPX accelerates MD for long-time simulation of viruses, as well as other large bionanosystems. By using universal inter-atomic force fields, it is generally applicable to all dynamical nanostructures and avoids the need of parameter recalibration with each new application. With our AMA method and MD/OPX, viral dynamics are predicted from laws of molecular physics via rigorous statistical mechanics