In vivo optogenetic identification and manipulation of GABAergic interneuron subtypes

Identification and manipulation of different GABAergic interneuron classes in the behaving animal are important to understand their role in circuit dynamics and behavior. The combination of optogenetics and large-scale neuronal recordings allows specific interneuron populations to be identified and perturbed for circuit analysis in intact animals. A crucial aspect of this approach is coupling electrophysiological recording with spatially and temporally precise light delivery. Focal multisite illumination of neuronal activators and silencers in predetermined temporal configurations or a closed loop manner opens the door to addressing many novel questions. Recent progress demonstrates the utility and power of this novel technique for interneuron research

Les hauts de Otesia

National audienceLorsqu'on parle d'intelligence artificielle, surgit très souvent le problème de la définition de ce que cette notion recouvre, en opposition ou en complément à une intelligence dite "humaine". Profitant des travaux sur la formalisation d'une intelligence mécanique et de nombreux outils développés dans ce cadre, abordons la question passionnante de la modélisation de l'intelligence humaine ! Regardons ici comment quelques scientifiques essayent d'aborder cette question

From computational neuroscience to computational learning science: modeling the brain of the learner and the context of the learning activity

International audienceWe share a new exploratory action known as Artificial Intelligence Devoted to Education (AIDE) launched with the support of Inria (Mnemosyne Team) and Nice INSPÉ from Côte d´Azur University (LINE laboratory) in connection with the Bordeaux NeuroCampus. It positions artificial intelligence in a somewhat original way ... not [only] as a disruptive tool, but as a formalism allowing to model learning human in problem-solving activities

Développement d'une ontologie pour l'analyse d'observables de l'apprenant dans le contexte d'une tâche avec des robots modulaires

The aim of this document is to present the design of an ontology allowing to carry out a modeling of the learner, the task and the observables during a learning activity, in order to develop a model applicable to the observed learning analytics which can be exploited to analyze them with computational approaches.The challenge here is to work from a relatively small batch of data (a few dozen to compare with the thousands of data used with classic statistical methods), highly structured, therefore to introduce a maximum of a priori information upstream to the analysis in order the results to be meaningful.The learner is modeled on the basis of knowledge from the educational science and cognitive neurosciences, including machine learning formalisms, in the very precise framework of a task, named CreaCube, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity.This document presents these elements and discusses the exploration and exploitation issues, the different goals (for example of performance, speed or mastery of the task), before relating this to the different types of memory and discussing the basics of problem solving, including engaging in a learning activity.It then describes the very precise construction of an ontology which formalizes this process of task resolution and knowledge construction, taking into account the stimuli received, the discovery of affordances, the setting of hypotheses, clearly distinguished from the notion of belief, without forgetting contextual knowledge.The production is shared as a free and open resource, and both the implications and the perspectives of this pioneering work of formalizing such a human learning task are discussed in conclusion.This research report and ontology corresponds to the short Post Doc research work of Lisa Roux, who is also the main author of the document, supervised by Margarida Romero and Frédéric Alexandre and was carried out within the framework of the Aex AIDE project supported by the Otesia Observatory of Technological, Economic and Societal impacts of Artificial Intelligence and Digital Technology.Le but de ce document est de présenter la conception d'une ontologie permettant de réaliser une modélisation de la personne apprenante, de la tâche et des observables au cours de l'activité, ceci afin de développer un modèle applicable aux traces d'apprentissage qui puisse être exploité pour les analyser avec des approches computationnelles. L'enjeu est ici de travailler à partir d'un relativement petit lot de données (quelques dizaines à comparer aux milliers de données utilisées avec les méthodes statistiques classiques), fortement structurées, donc d'introduire un maximum d'informations a priori en amont de l'analyse pour permettre que les résultats soient significatifs.L'apprenant·e est modélisé·e à partir de connaissances issues des sciences de l'éducation et des neurosciences cognitives, y compris les formalismes d'apprentissage machine, dans le cadre très précis d'une tâche -dite « CreaCube »- d'initiation à la pensée informatique, présentée sous forme d'un problème ouvert, qui implique la résolution d'un problème et de faire appel à la créativité.Ce document présente ces éléments et discute les problématiques d'exploration et exploitation, les différents buts (par exemple de performance, de célérité ou de maîtrise de la tâche), avant de relier cela aux différents types de mémoire et de discuter les bases de la résolution de problèmes, et l'engagement dans une activité d'apprentissage.Il décrit ensuite la construction très précise d'une ontologie qui formalise ce processus de résolution de tâche et de construction de connaissances, prenant en compte les stimuli reçus, la découverte d'affordances, la pose d'hypothèses, bien distinguées de la notion de croyance, sans oublier les connaissances contextuelles.La production est mise en partage sous forme de ressource libre et ouverte, et on discute en conclusion à la fois les implications et les perspectives de ce travail pionnier de formalisation d'une telle tâche d'apprentissage humain.Ce rapport de recherche et l'ontologie correspond au travail de recherche de Lisa Roux, qui est aussi la principale autrice du document, encadrée par Margarida Romero et Frédéric Alexandre et a été réalisé dans le cadre du projet Aex AIDE soutenu par Otesia, l'Observatoire des impacts Technologiques, Économiques et Sociétaux de l'Intelligence Artificielle et du numérique

Formalizing Problem Solving in Computational Thinking : an Ontology approach

International audienceWe introduce the idea of a symbolic description of a complex human learning task, in order to contribute to better understand how we learn. The learner is modeled on the basis of knowledge from learning sciences with the contribution of cognitive neurosciences, including machine learning formalism, in the very precise framework of a task, named #CreaCube reviewed here, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity. We target problem-solving tasks using tangible interfaces for computational thinking initiation, and describe in details how we model the task and the learner behavior in this task, including goal-driven versus stimulus-driven behavior and the learner knowledge construction. We show how formalizing these elements using an ontology offers a well-defined computational model and the possibility of inferences about model elements, analyzing and predicting the learner behavior. This operationalization of a creative problem-solving task is still at a preliminary stage, but an effective proof of concept is described in this study

Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here we describe a system that allows high channel count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing head-stage that permits free behavior of small rodents. The system integrates multi-shank, high-density recording silicon probes, ultra-flexible interconnects and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electro-anatomical boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomical space. These methods will allow the investigation of circuit operations and behavior-dependent inter-regional interactions for testing hypotheses of neural networks and brain function

Individual and neighborhood-level socioeconomic characteristics in relation to smoking prevalence among black and white adults in the Southeastern United States: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Low individual-level socioeconomic status (SES) is associated with higher prevalence of cigarette smoking. Recent work has examined whether neighborhood-level SES may affect smoking behavior independently from individual-level measures. However, few comparisons of neighborhood-level effects on smoking by race and gender are available.</p> <p>Methods</p> <p>Cross-sectional data from adults age 40-79 enrolled in the Southern Community Cohort Study from 2002-2009 (19, 561 black males; 27, 412 black females; 6, 231 white males; 11, 756 white females) were used in Robust Poisson regression models to estimate prevalence ratios (PRs) and 95% confidence intervals (CI) for current smoking in relation to individual-level SES characteristics obtained via interview and neighborhood-level SES characteristics represented by demographic measures from US Census block groups matched to participant home addresses.</p> <p>Results</p> <p>Several neighborhood-level SES characteristics were modestly associated with increased smoking after adjustment for individual-level factors including lower percentage of adults with a college education and lower percentage of owner-occupied households among blacks but not whites; lower percentage of households with interest, dividends, or net rental income among white males; and lower percentage of employed adults among black females.</p> <p>Conclusions</p> <p>Lower neighborhood-level SES is associated with increased smoking suggesting that cessation programs may benefit from targeting higher-risk neighborhoods as well as individuals.</p

Mobile Air Quality Studies (MAQS) - an international project

Due to an increasing awareness of the potential hazardousness of air pollutants, new laws, rules and guidelines have recently been implemented globally. In this respect, numerous studies have addressed traffic-related exposure to particulate matter using stationary technology so far. By contrast, only few studies used the advanced technology of mobile exposure analysis. The Mobile Air Quality Study (MAQS) addresses the issue of air pollutant exposure by combining advanced high-granularity spatial-temporal analysis with vehicle-mounted, person-mounted and roadside sensors. The MAQS-platform will be used by international collaborators in order 1) to assess air pollutant exposure in relation to road structure, 2) to assess air pollutant exposure in relation to traffic density, 3) to assess air pollutant exposure in relation to weather conditions, 4) to compare exposure within vehicles between front and back seat (children) positions, and 5) to evaluate "traffic zone"- exposure in relation to non-"traffic zone"-exposure. Primarily, the MAQS-platform will focus on particulate matter. With the establishment of advanced mobile analysis tools, it is planed to extend the analysis to other pollutants including including NO2, SO2, nanoparticles, and ozone

Investigating individual- and area-level socioeconomic gradients of pulse pressure among normotensive and hypertensive participants

Socioeconomic status is a strong predictor of cardiovascular disease. Pulse pressure, the difference between systolic and diastolic blood pressure, has been identified as an important predictor of cardiovascular risk even after accounting for absolute measures of blood pressure. However, little is known about the social determinants of pulse pressure. The aim of this study was to examine individual- and area-level socioeconomic gradients of pulse pressure in a sample of 2,789 Australian adults. Using data from the North West Adelaide Health Study we estimated the association between pulse pressure and three indices of socioeconomic status (education, income and employment status) at the area and individual level for hypertensive and normotensive participants, using Generalized Estimating Equations. In normotensive individuals, area-level education (estimate: −0.106; 95% CI: −0.172, −0.041) and individual-level income (estimate: −1.204; 95% CI: −2.357, −0.050) and employment status (estimate: −1.971; 95% CI: −2.894, −1.048) were significant predictors of pulse pressure, even after accounting for the use of medication and lifestyle behaviors. In hypertensive individuals, only individual-level measures of socioeconomic status were significant predictors of pulse pressure (education estimate: −2.618; 95% CI: −4.878, −0.357; income estimate: −1.683, 95% CI: −3.743, 0.377; employment estimate: −2.023; 95% CI: −3.721, −0.326). Further research is needed to better understand how individual- and area-level socioeconomic status influences pulse pressure in normotensive and hypertensive individuals.Lisa A. Matricciani, Catherine Paquet, Natasha J. Howard, Robert Adams, Neil T. Coffee, Anne W. Taylor and Mark Danie