Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models.

VOTESCRIPT: Telematic voting system designed to enable final count verification

In this paper we present a global description of a telematic voting system based on advanced cryptography and on the use of smart cards (VOTESCRIPT system) whose most outstanding characteristic is the ability to verify that the tally carried out by the system is correct, meaning that the results published by the system correspond with votes cast. The VOTESCRIPT system provides an individual verification mechanism allowing each Voter to confirm whether his vote has been correctly counted. The innovation with respect to other solutions lies in the fact that the verification process is private so that Voters have no way of proving what they voted in the presence of a non-authorized third party. Vote buying and selling or any other kind of extortion are prevented. The existence of the Intervention Systems allows the whole electoral process to be controlled by groups of citizens or authorized candidatures. In addition to this the system can simply make an audit not only of the final results, but also of the whole process. Global verification provides the Scrutineers with robust cryptographic evidence which enables unequivocal proof if the system has operated in a fraudulent way.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

An approach to visual cortex operation: optical neuron model

Several works have been published in the last years concerning the modelling and implementation of the visual cortex operation. Most of them present simple neurons with just two different responses, namely inhibitory and excitatory. Some of the different types of visual cortex cells are simulated in these configurations.

Some aspects of amacrine neuron simulation for motion detection

As it is known, there are five types of neurons in the mammalian retinal layer allowing the detection of several important characteristics of the visual image impinging onto the visual system, namely, photoreceptors, horizontal cells, amacrine, bipolar and ganglion cells. And it is a well known fact too, that the amacrine neuron architecture allows a first detection for objects motion, being the most important retinal cell to this function. We have already studied and simulated the Dowling retina model and we have verified that many complex processes in visual detection is performed with the basis of the amacrine cell synaptic connections. This work will show how this structure may be employed for motion detection

Identification of voting systems for the identification of preferences in public participation. Case Study: application of the Borda Count system for collective decision making at the Salonga National Park in the Democratic Republic of Congo.

Abstract:The aim of this paper is to review the literature on voting systems based on Condorcet and Borda. We compared and classified them. Also we referred to some strengths and weaknesses of voting systems and finally in a case study, we made use of the Borda voting system for collective decision making in the Salonga National Park in the Democratic Republic of Congo. Resumen: el objetivo de este trabajo es hacer una revisión bibliográfica de los sistemas de votación basados en Condorcet y Borda. Se ha comparado y clasificado los mismos. Así mismo se ha hecho referencia a algunas debilidades y fortalezas de los sistemas de votación y por último en un caso de estudio, se ha hecho uso del sistema de votación de Borda para la toma de decisión colectiva en el Parque Nacional de Salonga en la República Democrática del Congo.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

Intrastriatal injection of an adenoviral vector expressing glial-cell-line-derived neurotrophic factor prevents dopaminergic neuron degeneration and behavioral impairment in a rat model of Parkinson disease

Glial-cell-line-derived neurotrophic factor (GDNF) is a potent neurotrophic factor for adult nigral dopamine neurons in vivo. GDNF has both protective and restorative effects on the nigro-striatal dopaminergic (DA) system in animal models of Parkinson disease. Appropriate administration of this factor is essential for the success of its clinical application. Since it cannot cross the blood–brain barrier, a gene transfer method may be appropriate for delivery of the trophic factor to DA cells. We have constructed a recombinant adenovirus (Ad) encoding GDNF and injected it into rat striatum to make use of its ability to infect neurons and to be retrogradely transported by DA neurons. Ad-GDNF was found to drive production of large amounts of GDNF, as quantified by ELISA. The GDNF produced after gene transfer was biologically active: it increased the survival and differentiation of DA neurons in vitro. To test the efficacy of the Ad-mediated GDNF gene transfer in vivo, we used a progressive lesion model of Parkinson disease. Rats received injections unilaterally into their striatum first of Ad and then 6 days later of 6-hydroxydopamine. We found that mesencephalic nigral dopamine neurons of animals treated with the Ad-GDNF were protected, whereas those of animals treated with the Ad-β-galactosidase were not. This protection was associated with a difference in motor function: amphetamine-induced turning was much lower in animals that received the Ad-GDNF than in the animals that received Ad-β-galactosidase. This finding may have implications for the development of a treatment for Parkinson disease based on the use of neurotrophic factors.

Inactivation of the survival motor neuron gene, a candidate gene for human spinal muscular atrophy, leads to massive cell death in early mouse embryos

Proximal spinal muscular atrophy is an autosomal recessive human disease of spinal motor neurons leading to muscular weakness with onset predominantly in infancy and childhood. With an estimated heterozygote frequency of 1/40 it is the most common monogenic disorder lethal to infants; milder forms represent the second most common pediatric neuromuscular disorder. Two candidate genes—survival motor neuron (SMN) and neuronal apoptosis inhibitory protein have been identified on chromosome 5q13 by positional cloning. However, the functional impact of these genes and the mechanism leading to a degeneration of motor neurons remain to be defined. To analyze the role of the SMN gene product in vivo we generated SMN-deficient mice. In contrast to the human genome, which contains two copies, the mouse genome contains only one SMN gene. Mice with homozygous SMN disruption display massive cell death during early embryonic development, indicating that the SMN gene product is necessary for cellular survival and function.

Subplate neuron ablation alters neurotrophin expression and ocular dominance column formation

Ocular dominance column formation in visual cortex depends on both the presence of subplate neurons and the endogenous expression of neurotrophins. Here we show that deletion of subplate neurons, which supply glutamatergic inputs to visual cortex, leads to a paradoxical increase in brain-derived neurotrophic factor mRNA in the same region of visual cortex in which ocular dominance columns are absent. Subplate neuron ablation also increases glutamic acid decarboxylase-67 levels, indicating an alteration in cortical inhibition. These observations imply a role for this special class of neurons in modulating activity-dependent competition by regulating levels of neurotrophins and excitability within a developing cortical circuit.

Neural restrictive silencer factor recruits mSin3 and histone deacetylase complex to repress neuron-specific target genes

Accumulative evidence suggests that more than 20 neuron-specific genes are regulated by a transcriptional cis-regulatory element known as the neural restrictive silencer (NRS). A trans-acting repressor that binds the NRS, NRSF [also designated RE1-silencing transcription factor (REST)] has been cloned, but the mechanism by which it represses transcription is unknown. Here we show evidence that NRSF represses transcription of its target genes by recruiting mSin3 and histone deacetylase. Transfection experiments using a series of NRSF deletion constructs revealed the presence of two repression domains, RD-1 and RD-2, within the N- and C-terminal regions, respectively. A yeast two-hybrid screen using the RD-1 region as a bait identified a short form of mSin3B. In vitro pull-down assays and in vivo immunoprecipitation-Western analyses revealed a specific interaction between NRSF-RD1 and mSin3 PAH1-PAH2 domains. Furthermore, NRSF and mSin3 formed a complex with histone deacetylase 1, suggesting that NRSF-mediated repression involves histone deacetylation. When the deacetylation of histones was inhibited by tricostatin A in non-neuronal cells, mRNAs encoding several neuronal-specific genes such as SCG10, NMDAR1, and choline acetyltransferase became detectable. These results indicate that NRSF recruits mSin3 and histone deacetylase 1 to silence neural-specific genes and suggest further that repression of histone deacetylation is crucial for transcriptional activation of neural-specific genes during neuronal terminal differentiation.

X-ngnr-1 and Xath3 promote ectopic expression of sensory neuron markers in the neurula ectoderm and have distinct inducing properties in the retina

Xath3 encodes a Xenopus neuronal-specific basic helix–loop–helix transcription factor related to the Drosophila proneural factor atonal. We show here that Xath3 acts downstream of X-ngnr-1 during neuronal differentiation in the neural plate and retina and that its expression and activity are modulated by Notch signaling. X-ngnr-1 activates Xath3 and NeuroD by different mechanisms, and the latter two genes crossactivate each other. In the ectoderm, X-ngnr-1 and Xath3 have similar activities, inducing ectopic sensory neurons. Among the sensory-specific markers tested, only those that label cranial neurons were found to be ectopically activated. By contrast, in the retina, X-ngnr-1 and Xath3 overexpression promote the development of overlapping but distinct subtypes of retinal neurons. Together, these data suggest that X-ngnr-1 and Xath3 regulate successive stages of early neuronal differentiation and that, in addition to their general proneural properties, they may contribute, in a context-dependent manner, to some aspect of neuronal identity.

Effect of β-amyloid block of the fast-inactivating K+ channel on intracellular Ca2+ and excitability in a modeled neuron

β-Amyloid peptide (Aβ), one of the primary protein components of senile plaques found in Alzheimer disease, is believed to be toxic to neurons by a mechanism that may involve loss of intracellular calcium regulation. We have previously shown that Aβ blocks the fast-inactivating potassium (A) current. In this work, we show, through the use of a mathematical model, that the Aβ-mediated block of the A current could result in increased intracellular calcium levels and increased membrane excitability, both of which have been observed in vitro upon acute exposure to Aβ. Simulation results are compared with experimental data from the literature; the simulations quantitatively capture the observed concentration dependence of the neuronal response and the level of increase in intracellular calcium.

A relationship between behavior, neurotrophin expression, and new neuron survival

The high vocal center (HVC) controls song production in songbirds and sends a projection to the robust nucleus of the archistriatum (RA) of the descending vocal pathway. HVC receives new neurons in adulthood. Most of the new neurons project to RA and replace other neurons of the same kind. We show here that singing enhances mRNA and protein expression of brain-derived neurotrophic factor (BDNF) in the HVC of adult male canaries, Serinus canaria. The increased BDNF expression is proportional to the number of songs produced per unit time. Singing-induced BDNF expression in HVC occurs mainly in the RA-projecting neurons. Neuronal survival was compared among birds that did or did not sing during days 31–38 after BrdUrd injection. Survival of new HVC neurons is greater in the singing birds than in the nonsinging birds. A positive causal link between pathway use, neurotrophin expression, and new neuron survival may be common among systems that recruit new neurons in adulthood.

Survival motor neuron protein modulates neuron-specific apoptosis

Spinal muscular atrophy (SMA) is attributed to mutations in the SMN1 gene, leading to loss of spinal cord motor neurons. The neurotropic Sindbis virus vector system was used to investigate a role for the survival motor neuron (SMN) protein in regulating neuronal apoptosis. Here we show that SMN protects primary neurons and differentiated neuron-like stem cells, but not cultured cell lines from virus-induced apoptotic death. SMN also protects neurons in vivo and increases survival of virus-infected mice. SMN mutants (SMNΔ7 and SMN-Y272C) found in patients with SMA not only lack antiapoptotic activity but also are potently proapoptotic, causing increased neuronal apoptosis and animal mortality. Full-length SMN is proteolytically processed in brains undergoing apoptosis or after ischemic injury. Mutation of an Asp-252 of SMN abolished cleavage of SMN and increased the antiapoptotic function of full-length SMN in neurons. Taken together, deletions or mutations of the C terminus of SMN that result from proteolysis, splicing (SMNΔ7), or germ-line mutations (e.g., Y272C), produce a proapoptotic form of SMN that may contribute to neuronal death in SMA and perhaps other neurodegenerative disorders.