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An R package that solves a series of initial value problems in C via the GNU scientific library (ode solvers).
The C code calls gsl_odeiv2 module functions to solve the problem or problems. The goal is to offload as much work as possible to the C code and keep the overhead minimal. That is why this package expects to solve a set of problems, rather than one, for the same model file (varying in e.g.: initial conditions, or parameters).
The package contains 3 interface functions, they accept different ways of defining a set of problems,each with their own drawbacks and advantages. The interface functions are described in the following Sections.
The ODE has to exist as a shared library (.so) file (currently in the current working directory: ?setwd and ?getwd ). There are some assumptions we make about the contents of the shared library file.
Here we assume that the solutions serve some scientific purpose and the lab experiments come with observables, some measureable values that depend on the system's state (but are not the full state vector). We call the part of the model that calculates the observables ${ModelName}_func() (vfgen also calls them Functions of the model).

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3DSpineS

Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.

Data analysis and visualisationData

Android app for multimodal data acquisition from wearables

An app for acquiring and storing data from multiple sensors. Currently, can be used with the following devices: Empatica E4 Tablet/Smartphone built-in sensors MetaMotion R In order to improve reliability, a bipartite structure has been implemented. In particular, the Main Activity acts as an interface between the user and the main service that constitutes the principal actor. The latter performs scans, handles the user's requests to connect remote devices, all the unexpected disconnection that may happen and receives the data from the wireless sensors.

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Bids Manager & Pipeline

Manually driven processes for data storing can lead to human errors, which cannot be tolerated in the context of a clinical data sets. The Bids manager offers a secure system to import and structure patient’s clinical data sets in Brain Imaging Data Structure (BIDS). BIDS is an initiative aiming at establishing a common standard to describe data and its organization on disk for both neuroimaging and electrophysiological data. Bids Manager is a software for clinicians and researchers with a user-friendly interface.

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