Events in March 2017
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Digital Health, Biology and Earth: Part I
Digital Health, Biology and Earth: Part I
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March 13, 2017Chairman
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Talk 1
Title:
DreamTech : project management using GitFlowSpeaker:
Thibaud Kloczko -SED SophiaAbstract:
Git is a very powerful tool providing a very efficient branching system. However, using git is not straightforward and when one wants to carry out an efficient workflow to manage a project, it is necessary to use several complex commands so that nobody is able to follow the workflow. This Dreamtech aims at presenting the workflow that we carry out in all our software projects and the tool, namely gitflow, that we use to make it easier to follow. As usual, we only carry out standard models so one could find many details about gitflow on the Web. Here are the main ones: -the reference post of Vincent Driessen. -the post of Synbioz that we use as canvas for this presentation -the website semver that presents the semantic versionning that one can easily couple to gitflow. Finally, the best guide about GitFlow is provided by Atlassian.Talk 2
Title:
Influence of skull modelling on conductivity estimation for EEG source analysisSpeaker:
Christos Papageorgakis - Athena/ApicsAbstract:
The skull conductivity strongly influences the accuracy of EEG source localization methods. As the conductivity of the skull has strong inter-individual variability, conductivity estimation techniques are required. Typically, conductivity estimation is performed on data from a single event-related stimulation paradigm, which can be explained by one dipole source. A conductivity value for the skull can be estimated as the value for which the single dipole source provides the best goodness of fit to the data. This conductivity value is then used to analyse the actual data of interest. It is known that the optimal local skull conductivity when modelling the skull as one compartment depends on the amount of spongiosa present locally. The research question arising is: Is conductivity estimation based on data from a single paradigm meaningful without accounting for the internal skull structure? -
Digital Health, Biology and Earth:Part II
Digital Health, Biology and Earth:Part II
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March 27, 2017Chairman
Adam Cooman - Apics
Announcement
Valerie Francois: MathC2+
Talk 1
Title:
Modifying a biologically inspired retina simulator to reconstruct realistic responses to moving stimuliSpeaker:
Selma Souehil-BiovisionAbstract:
Virtual Retina is a retina emulator based on a detailed model, that transforms a stimulus into a set of spike trains, as those emitted by retinal ganglion cells. The model includes three stages of processing : the photoreceptors, the bipolar cells and the ganglion cells. The gain control mechanism implemented in the bipolar layer has been adapted in order to reproduce motion anticipation results from Berry & al. (1999). Then a second gain control mechanism has been implemented in the ganglion layer to account for two other features of motion processing : alert response and motion onset. In this case, a simple pooling of bipolar cells by ganglion cells (short-range connectivity) is enough to reconstruct the expected responses. Responses to more complex stimuli (eg. random motion), however, require to add long-range connectivity.Talk 2
Title:
Understanding the mechanisms of retinal waves: modeling and experimentsSpeaker:
Dora Karvouniari - BiovisionAbstract:
Retinal waves are spontaneous bursts of activity propagating in the developing retina, found to play a central role in shaping the visual system and retinal circuitry. They occur at early stages of development and gradually disappear upon maturation, just before vision is functional. Waves during their second stage (II), are mediated by the combination of autonomous bursting of starburst amacrine cells and their mutual coupling through the neurotransmitter acetylcholine. We propose a biophysical model of the spontaneous bursting coupled starburst amacrine cells during development, suitable for a mathematical analysis using non-linear dynamical systems tools. Based on our model, we predict that the spatiotemporal features of these waves change depending on one physiological parameter, the coupling strength between cells. To test this hypothesis, we performed multi-electrode array recordings on perinatal mice, varying pharmacologically the strength of the cholinergic coupling.
