Data Fusion BAUE Estimation of a deterministic vector, applications to image noise and blur reduction

In this work we conceive centralized data fusion as a deterministic parameter estimation problem. Two different criterions are compared: best affine unbiased fusion rule (BAUE), and Maximum Likelihood for Gaussian measurement noise. Estimates are described in terms of their covariance matrices, the Cramer-Rao lower bound and simulations. The developed fusion rules are suited to two different image fusion cases: noise reduction under differently exposed images, and blur reduction based on lens response knowledge.Sociedad Argentina de Informática e Investigación Operativ

Robust methods for background extraction in video

In this paper a framework is presented to automatically extract a sequence of images of the background of a scene from a shaky film. That is, the input video sequence may have local and global motion but the output video must contain exclusively the static background scene. Applying robust procedures to this end is one of the main goals of this work, since the aim is to get a procedure not only resistant to low scale noise but to occasional high scale noise. The median is used as an estimate of the background, the median absolute deviation (MAD) is used to establish a threshold to locate foreground and M-estimation for regression is used to stabilize the video sequence.Sociedad Argentina de Informática e Investigación Operativ

Modelo de equilibrio general computado para la Argentina 2006

Se presenta aquí el resultado del proyecto Picto-Crup para la SECYT, en el que se extendió y actualizó el modelo de equilibrio general computable de la economía argentina. En este caso el  modelo se calibró para 2006. La evaluación potencial de la política económica requiere de herramientas que permitan estimar su impacto sobre los precios relativos, de modo que se pueda tener en cuenta su repercusión sobre toda la economía. La medición de los efectos da las políticas públicas y shocks macroeconómicos constituye un requisito fundamental en el ámbito de política económica. Los modelos de equilibrio general computado (MEGC) constituyen una herramienta sumamente útil para la simulación de las consecuencias de las mencionadas políticas sobre la economía. A diferencia de la metodología de equilibrio parcial, herramienta tradicionalmente usada para el estudio de casos sectoriales, el análisis de equilibrio general no divide a la economía en compartimientos estancos. Es decir, la economía es analizada en conjunto de acuerdo a los diferentes nexos entre sectores y agentes económicos. Además supone la explicación del ciclo de la generación de ingresos en la economía a partir de los precios y de las remuneraciones de los factores, todas variables endógenas. Esto permite entonces extender o modificar las predicciones de primer orden de los modelos sectoriales para así incorporar efectos de segundo orden, todos ellos consistentes con las funciones de comportamiento establecidas y restricciones presupuestarias de los agentes. Los MEGC están basados en una matriz de contabilidad social (MCS) o SAM (Social Accounting Matrix). La MCS representa los flujos de bienes y servicios para una economía en un período dado. El ejercicio de construcción de una MCS tiene dos notorias ventajas. La primera de ellas, es la organización de información acerca de la economía y de la estructura social de un país o una región durante un lapso particular de manera consistente. La segunda, es que la MCS provee las bases estadísticas para la creación de un modelo que permita simular distintas políticas públicas, de modo que sea compatible con la teoría bien fundada Es decir, una vez que la información de una región en un año en particular ha sido organizada en la forma de una MCS, ésta se representa con imágenes estáticas y dinámicas que revelan una gran parte de la estructura económica. El objetivo principal de este estudio es el de elaborar un modelo económico, en nuestro caso, un modelo de equilibrio general computado para la Argentina en 2006 que nos permita evaluar las políticas económicas. Tiene una base teórica con sentido aplicado. La experimentación computacional es un instrumento útil para aprender, evaluar alternativas y evitar el arrepentimiento. El modelo construido permite hacer ejercicios de sensibilidad. Para ello, se pueden suponer valores alternativos de las elasticidades de sustitución en el consumo y en la producción, como así también, a la movilidad de factores entre sectores de la economía y con el resto del mundo. La estructura de este trabajo es la siguiente. Las primeras dos secciones enuncian las características básicas de los MEGC y la teoría subyacente a dicho modelo. La tercera y cuarta secciones describen la elaboración de la MCS, como así también los métodos pertinentes para lograr su respectivo ajuste. La quinta y sexta sección discuten el diseño de los MEGC, tanto en su versión estática como dinámica para que, luego de establecida la calibración, se realice una serie de ejercicios contrafácticos que nos permitirán obtener una aproximación a la forma de comportarse de la economía Argentina: Finalmente, las últimas dos secciones contemplan la elaboración de microsimulaciones y comentarios generales.A computable general equilibrium model, dynamic and recursive, for the economy of Argentina as of 2006 is presented in this document. It is a model of 29 sectors of production, ten consumers, government and rest of the world. It is a model of a small economy, in a GAMS/MPSGE environment. The document includes a thorough description of: 1) the Social Accounting Matrix, 2) the Calibration to the benchmark year, 3) the Simulations and 4) the output files in terms of national accounts, welfare levels and results for firms and sectors. Several simulations of exogenous and policy induced shocks are also presented.Fil: Chisari, Omar Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Ferro, Gustavo Adolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: González, Mariano Ezequiel. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Leon, Sonia Mabel. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Maquieyra, Javier A.. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Mastronardi, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Roitman, Mauricio Ezequiel. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Romero, Carlos A.. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; ArgentinaFil: Theller, Ricardo M.. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; Argentin

Changes of Mind in an Attractor Network of Decision-Making

Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks. Especially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes them from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we demonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the choice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task. Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the network predicts neural activity during changes of mind and accurately simulates reaction times, performance and percentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation, which up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor from linear diffusion models

A Fluctuation-Driven Mechanism for Slow Decision Processes in Reverberant Networks

The spike activity of cells in some cortical areas has been found to be correlated with reaction times and behavioral responses during two-choice decision tasks. These experimental findings have motivated the study of biologically plausible winner-take-all network models, in which strong recurrent excitation and feedback inhibition allow the network to form a categorical choice upon stimulation. Choice formation corresponds in these models to the transition from the spontaneous state of the network to a state where neurons selective for one of the choices fire at a high rate and inhibit the activity of the other neurons. This transition has been traditionally induced by an increase in the external input that destabilizes the spontaneous state of the network and forces its relaxation to a decision state. Here we explore a different mechanism by which the system can undergo such transitions while keeping the spontaneous state stable, based on an escape induced by finite-size noise from the spontaneous state. This decision mechanism naturally arises for low stimulus strengths and leads to exponentially distributed decision times when the amount of noise in the system is small. Furthermore, we show using numerical simulations that mean decision times follow in this regime an exponential dependence on the amplitude of noise. The escape mechanism provides thus a dynamical basis for the wide range and variability of decision times observed experimentally

Complexity reduction of rate-equations models for two-choice decision-making

We are concerned with the complexity reduction of a stochastic system of differential equations governing the dynamics of/na neuronal circuit describing a decision-making task. This reduction is based on the slow-fast behavior of the problem and/nholds on the whole phase space and not only locally around the spontaneous state. Macroscopic quantities, such as/nperformance and reaction times, computed applying this reduction are in agreement with previous works in which the/ncomplexity reduction is locally performed at the spontaneous point by means of a Taylor expansion.J.A. Carrillo acknowledges support from the Royal Society by a Wolfson Research Merit Award and by the Engineering and Physical Sciences Research Council grant with references EP/K008404/1. J.A. Carrillo was partially supported by the project MTM2011-27739-C04-02 DGI (Spain) and 2009-SGR-345 from AGAUR-Generalitat de Catalunya. S. Cordier and S. Mancini acknowledge support by the ANR project MANDy, Mathematical Analysis of Neuronal Dynamics, ANR-09-BLAN-0008-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Multiple choice neurodynamical model of the uncertain option task

The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky perceptual decision. We propose a multiple choice model implemented in a discrete attractors network. This model is able to reproduce both behavioral and neurophysiological experimental data and therefore provides support to the numerous perspectives that interpret the uncertain option task as a sensory-motor association. The model explains the behavioral and neural data recorded in monkeys as the result of the multistable attractor landscape and produces several testable predictions. One of these predictions may help distinguish our model from a recently proposed continuous attractor model.This work was supported by MINECO (PSI2013-42091-P), Agència de Gestio d'Ajuts Universitaris i de Recerca (AGAUR-2014SGR856), European Research Council (ERC) Advanced Grant DYSTRUCTURE (n. 295129), FlagERA ChampMouse PCIN-2015- 127 and FET-Flagship HPB-SGA1 (720270). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The encoding of decision difficulty and movement time in the primate premotor cortex

Estimating the difficulty of a decision is a fundamental process to elaborate complex and adaptive behaviour. In this paper, we show that the movement time of behaving monkeys performing a decision-making task is correlated with decision difficulty and that the activity of a population of neurons in ventral Premotor cortex correlates with the movement time. Moreover, we found another population of neurons that encodes the discriminability of the stimulus, thereby supplying another source of information about the difficulty of the decision. The activity of neurons encoding the difficulty can be produced by very different computations. Therefore, we show that decision difficulty can be encoded through three different mechanisms: 1. Switch time coding, 2. rate coding and 3. binary coding. This rich representation reflects the basis of different functional aspects of difficulty in the making of a decision and the possible role of difficulty estimation in complex decision scenarios.MMG was supported by the FP7-ICT BrainScales. AI was supported by the FI/AGAUR fellowship and FP7-ICT Coronet (n. 269459). MP was supported by the CONSOLIDER-INGENIO 2010 Program CSD2007-00012. JLPV was supported by the Human Frontier Science Programme (LT000442/2012). GD was supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), the FP7- FET-Flagship Human Brain Project (n. 604102) and the Plan Estatal de Fomento de la investigación Científica y Técnica de Excelencia (PSI2013-42091-P)

The influence of spatiotemporal structure of noisy stimuli in decision making

Decision making is a process of utmost/nimportance in our daily lives, the study of which has/nbeen receiving notable attention for decades. Nevertheless,/nthe neural mechanisms underlying decision making/nare still not fully understood. Computational modeling/nhas revealed itself as a valuable asset to address some of/nthe fundamental questions. Biophysically plausible models,/nin particular, are useful in bridging the different levels/nof description that experimental studies provide, from the/nneural spiking activity recorded at the cellular level to the/nperformance reported at the behavioral level. In this/narticle, we have reviewed some of the recent progress/nmade in the understanding of the neural mechanisms that/nunderlie decision making. We have performed a critical/nevaluation of the available results and address, from a/ncomputational perspective, aspects of both experimentation/nand modeling that so far have eluded comprehension./nTo guide the discussion, we have selected a central/ntheme which revolves around the following question: how/ndoes the spatiotemporal structure of sensory stimuli affect/nthe perceptual decision-making process? This question is a/ntimely one as several issues that still remain unresolved/nstem from this central theme. These include: (i) the role of/nspatiotemporal input fluctuations in perceptual decision/nmaking, (ii) how to extend the current results and models/nderived from two-alternative choice studies to scenarios/nwith multiple competing evidences, and (iii) to establish/nwhether different types of spatiotemporal input fluctuations/naffect decision-making outcomes in distinctive ways./nAnd although we have restricted our discussion mostly to/nvisual decisions, our main conclusions are arguably/ngeneralizable; hence, their possible extension to other/nsensory modalities is one of the points in our discussion.AI acknowledges funding from the SUR, DEC of the Generalitat de/nCatalunya and FSE. LDM is a Ramon y Cajal Fellow and acknowledges funding/nfrom the Ministry of Science and Innovation through the Ramon y Cajal/nprogramme. She also acknowledges financial support from the research project/nTIN2010-21771-C02-02 funded by the Ministry of Science and Innovation. MP was/nsupported by the CONSOLIDER-INGENIO 2010 Program CSD2007-00012. GD was/nsupported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish/nResearch Project SAF2010-16085 and by the CONSOLIDER-INGENIO 2010 Program/nCSD2007-00012, and the FP7-ICT BrainScales and Coronet. RR was supported by/ngrants from the Dirección de Personal Académico de la Universidad Nacional/nAutónoma de México and the Consejo Nacional de Ciencia y Tecnología