Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

How Porous are the Walls that Separate Us?: Transformative Service-Learning, Women’s Incarceration, and the Unsettled Self

In this article, we refine a politics of thinking from the margins by exploring a pedagogical model that advances transformative notions of service learning as social justice teaching. Drawing on a recent course we taught involving both incarcerated women and traditional college students, we contend that when communication among differentiated and stratified parties occurs, one possible result is not just a view of the other but also a transformation of the self and other. More specifically, we suggest that an engaged feminist praxis of teaching incarcerated women together with college students helps illuminate the porous nature of fixed markers that purport to reveal our identities (e.g., race and gender), to emplace our bodies (e.g., within institutions, prison gates, and walls), and to specify our locations (e.g., cultural, geographic, socialeconomic). One crucial theoretical insight our work makes clear is that the model of social justice teaching to which we aspired necessitates re-conceptualizing ourselves as students and professors whose subjectivities are necessarily relational and emergent.

Student2Student als Organisationsmodell für die Entwicklung von E-Learning-Ressourcen

Die E-Learning-Plattform VBA@HfTL unterstützt das Erlernen von grundlegenden Programmierkonzepten mithilfe der Programmiersprache Visual Basic for Applications (VBA). Diese Plattform wurde von Studierenden für Studierende der Fachrichtung Wirtschaftsinformatik entwickelt, so dass ein Student2Student (S2S)-Ansatz umgesetzt wurde. Der Beitrag führt die konzeptionellen Grundlagen dieses Ansatzes ein und erläutert die organisatorischen sowie technischen Rahmenbedingungen des Entwicklungsprojekts als Forschungsfallstudie. Das Projektergebnis zeigt, dass Studierende selbstorganisiert E-Learning-Ressourcen entwickeln und sich dabei interdisziplinäre Fachinhalte der Wirtschaftsinformatik aneignen können. Die resultierende E-Learning-Plattform liefert aufgrund der hohen Resonanz nicht nur einen wertvollen Beitrag zur Unterstützung von Lernprozessen in der Aus- und Weiterbildung, sondern bietet der Hochschule auch eine Möglichkeit zur Profilierung des Bildungsangebots im Rahmen der Öffentlichkeitsarbeit.

Competence Monitoring in Project Teams by using Web based portfolio management systems

This paper reports a learning experience related to the acquisition of project management competences. Students from three different universities and backgrounds, cooperate in a common project that drives the learning-teaching process. Previous related works on this initiative have already evaluated the goodness of this multidisciplinary, project-based learning approach in the context of a new educative paradigm. Yet the innovative experience has allowed the authors to define a rubric in order to measure specific competences in project management. The study shows the rubric’s main aspects as well as competence acquisition evaluation alternatives, based in the metrics defined. Key indicators and specific reports obtained from data base fields in the web tool will support this work. As a result, new competences can be assessed, such ones like teamwork, problem solving, communication and leadership. Final goal is to provide an overall competence map to the students at the same time they improve their skills.

Monitoring the communication competence in an innovative context of project management learning

This document presents an innovative, formal educational initiative that is aimed at enhancing the development of engineering students' specific competences. The subject of project management is the common theoretical and practical framework that articulates an experience that is carried out by multidisciplinary groups. Full utilization of Web 2.0 platforms and Project Based Learning constitutes the applied methodology. More specifically, this study focuses on monitoring communication competence when working in virtual environments, providing an ad-hoc rubric as a final result.

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.

A Web-based Support System for Providing Effective Monitoring, Feedback and Evaluation in Project Management Education

This work introduces a web-based learning environment to facilitate learning in Project Management. The proposed web-based support system integrates methodological procedures and information systems, allowing to promote learning among geographically-dispersed students. Thus, students who are enrolled in different universities at different locations and attend their own project management courses, share a virtual experience in executing and managing projects. Specific support systems were used or developed to automatically collect information about student activities, making it possible to monitor the progress made on learning and assess learning performance as established in the defined rubric.

O ESTUDANTE DA EAD (EDUCAÇÃO A DISTÂNCIA): UM ESTUDO DE PERFIL E INTERAÇÃO GERACIONAL

A aprendizagem formal e tradicional tem dado lugar a um cenário desafiador no qual educador e educando não comungam do mesmo espaço físico. A Educação a Distância (EAD), ainda é vista como uma solução que agrega cada vez mais alunos de diferentes idades que desejam uma graduação de ensino superior ou a continuidade dela. A pesquisa com o título: “O estudante da EAD (educação a distância): um estudo de perfil e interação geracional” propõe conhecer as características do perfil atual do estudante da EAD, abordando o diálogo entre as gerações no ambiente social escolar. O enfoque da pesquisa é qualitativa, exploratória e descritiva com dados que foram coletados através de entrevista com 08 alunos das gerações X e Y para assim entender se este perfil tem sido renovado com alunos mais jovens, do que a faixa etária de 25 a 45 anos. O resultado demonstra que alunos na faixa de 17 a 24 anos a cada ano aumentam 1% das matrículas. Já a faixa de 25 a 45 anos prevalece com 70% das matrículas. Portanto, este resultado revela que o perfil do aluno EAD ainda é o do jovem adulto, para adulto mais experiente, que busca a graduação com o propósito de progressão no ambiente profissional. As duas gerações citadas geração X e geração Y, mesmo em contextos históricos diferenciados de valores, crenças e comportamentos participam atualmente de uma transformação social que contempla os meios de produção do trabalho, a formação educacional e as relações sociais. O diálogo intergeracional direciona a um aprendizado compartilhado, participativo na troca de experiências mutuas. Para a geração X o jovem atual não é mais nomeado como o que precisa escutar e aprender, mas tem muito a partilhar, principalmente diante da facilidade com os meios tecnológicos. E para a geração Y, na partilha não há barreiras de idade, mas a segurança de interagir e se comunicar diante da troca de experiências

Making it easy to do the right thing in healthcare: Advancing improvement science education through accredited pan European higher education modules

Background: Numerous international policy drivers espouse the need to improve healthcare. The application of Improvement Science has the potential to restore the balance of healthcare and transform it to a more person-centred and quality improvement focussed system. However there is currently no accredited Improvement Science education offered routinely to healthcare students. This means that there are a huge number of healthcare professionals who do not have the conceptual or experiential skills to apply Improvement Science in everyday practise. Methods: This article describes how seven European Higher Education Institutions (HEIs) worked together to develop four evidence informed accredited inter-professional Improvement Science modules for under and postgraduate healthcare students. It outlines the way in which a Policy Delphi, a narrative literature review, a review of the competency and capability requirements for healthcare professionals to practise Improvement Science, and a mapping of current Improvement Science education informed the content of the modules. Results: A contemporary consensus definition of Healthcare Improvement Science was developed. The four Improvement Science modules that have been designed are outlined. A framework to evaluate the impact modules have in practise has been developed and piloted. Conclusion: The authors argue that there is a clear need to advance healthcare Improvement Science education through incorporating evidence based accredited modules into healthcare professional education. They suggest that if Improvement Science education, that incorporates work based learning, becomes a staple part of the curricula in inter-professional education then it has real promise to improve the delivery, quality and design of healthcare.

An Investigation of the Features and Effectiveness of the Full-Day Early Learning Kindergarten Program in Ontario

This dissertation includes two studies. Study 1 is a qualitative case study that describes enactment of the main components of a high fidelity Full-Day Early Learning Kindergarten (FDELK) classroom, specifically play-based learning and teacher-ECE collaboration. Study 2 is a quantitative analysis that investigates how effectively the FDELK program promotes school readiness skills, namely self-regulation, literacy, and numeracy, in Kindergarteners. To describe the main components of an FDELK classroom in Study 1, a sub-sample of four high fidelity case study schools were selected from a larger case study sample. Interview data from these schools’ administrators, educators, parents, and community stakeholders were used to describe how the main components of the FDELK program enabled educators to meet the individual needs of students and promote students’ SR development. In Study 2, hierarchical regression analyses of 32,207 students’ self-regulation, literacy, and numeracy outcomes using 2012 Ontario Early Development Instrument (EDI) data revealed essentially no benefit for students participating in the FDELK program when compared to peers in Half-Day or Alternate-Day Kindergarten programs. Being older and female predicted more positive SR and literacy outcomes. Age and gender accounted for limited variance in numeracy outcomes. Results from both studies suggest that the Ontario Ministry of Education should take steps to improve the quality of the FDELK program by incorporating evidence-based guidelines and goals for play, reducing Kindergarten class sizes to more effectively scaffold learning, and revising curriculum expectations to include a greater focus on SR, literacy, and numeracy skills.

Negotiating, Communicating, Collaborating: A Sociocultural View of Online Program Development

Thesis (Ph.D.)--University of Washington, 2016-05

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06

Online conference case study: Creating a comfort zone within an emerging community of practice

This paper outlines the context and theoretical basis for the design, implementation and evaluation of an online conference conducted in 2003. The purpose of the conference was to provide postgraduate distance learners with an opportunity to interact with human factors and healthcare professionals, thereby providing them with exposure to this emerging community of practice. The conference was delivered through a WebCT site and stimulated various modes of interaction. The paper discusses the design and format of the conference and details an analysis of the online transcript that shows development of learning communities as “comfort zones” within which the participants could communicate in a common language and atmosphere of understanding over the 2 days that the conference ran.

Fostering professional development through integrating work and formal learning

The Undergraduate Site Learning Program (USLP) is an innovative work-based learning program that addresses the call to develop a broader set ofattributes in engineering graduates. Unlike cooperative education programs, site learning can give students full academic credit for their placement without extending the duration of the degree through the use of an innovative learning alignment model. A cenrralpart ofthis program is a unique course entitled Professional Development in which students articulate and reflect upon the lessons they leom while on placement in industry. Students spend the bulk ofa semester on-site often in remote locations, which requires a flexible approach to course operation and fosters independent learning. Thus the USLP challenges both staff and students and produces outcomes that bofh the alumni and industry value.