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  • Unité d'enseignement : Computational neurosciences
Nombre de crédits de l'UE : 6
Code APOGEE : BIO2563M
    Responsabilité de l'UE :
DI VOLO MATTEO
 matteo.di-volouniv-lyon1.fr
    Type d'enseignement
Nb heures *
Cours Magistraux (CM)
30 h
Travaux Dirigés (TD)
6 h

* Ces horaires sont donnés à titre indicatif.

    Pré-requis :

-        Knowledge in coding (Matlab or Python) and applied mathematics (probabilities, statistics, linear algebra)

-        Knowledge in neurophysiology and cognitive neurosciences

    Compétences attestées (transversales, spécifiques) :

Transversales

 -        Know how to chair a scientific discussion

-        Know how to bring innovative contributions within specialized scientific discussions and in an international context.

 

Specifiques

-        Identify and apply relevant techniques to analyze and quantify a human behavior

-        Develop a multidisciplinary approach in cognitive neurosciences combining applied mathematics, computer sciences, neuroimaging, electrophysiology and behavioral sciences.

-        Build scientific hypotheses, elaborate a protocol, select methodological approaches.

-        Pursue a scientific study in order to model brain functions.

-        Mobilize highly specialized knowledge, including cutting-edge technologies and most recent discoveries in computational neurosciences

-        Develop critical reasoning on a topic related to computational neurosciences and/or at the interface between cognitive neurosciences and applied mathematics

    Programme de l'UE / Thématiques abordées :

Computational neuroscience aims to understand the brain through theoretical, mathematical and computational means. This field is in full expansion leading to new tools to understand and study the brain at different scales, to simulate neuronal and cognitive mechanisms, and to build programs or machines having properties mimicking those of the nervous system.

The aim of this course is to familiarize students with scientific modelling in the field of Neuroscience, as well as with the modern tools underlying this approach. This course is meant to be accessible to biologists, psychologists, engineers, as well as clinicians. This training course shows the generic nature of this approach through heterogeneous examples. It emphasizes the tight link between modelling and empirical work: from the early formulation of hypotheses, with qualitative and quantitative predictions, the challenge of optimizing the experimental design (including simulations), data analysis using mathematical models (model validation, inference, model comparison, parameter estimation, conclusion and report of results). As an extension of the M1 NeuroComp training course, this teaching mixes theoretical courses and group works, covering both physiological and cognitive aspects of brain activity. Conference series are given by national and international experts. Extensive time is dedicated to discussions between the students and the speakers, aiming to develop in-depth scientific reasoning and networking.
    Parcours / Spécialité / Filière / Option utilisant cette UE :
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