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Theoretical Neuroscience

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M2_E33_TheoNeuro_schedule 2021-2022

Master in Life Science, ENS
Bio-M2_E33 | Theoretical Neuroscience
Year and Smester : M2 | S1
Where : Biology department, ENS
Duration : 39h CM + 18h TD
First and last day of class : from September 22nd to December 15th, 2022
Hours : Every Thursday 14:00-17:30 (12 sessions)
Maximum class size : 25 students

Coordination

Jean-Pierre Nadal, Centre d’analyse et de Mathématiques sociales, EHESS et Laboratoire de Physique, ENS.
Vincent Hakim, Laboratoire de Physique, ENS.

Credits

6 ECTS

Keywords

Computational Neuroscience | Mathematics and Neuroscience | Modeling | Cognitive Science.

Course prerequisites

Ease with Mathematics.

Course objectives and description

Thèmes : This course introduces a range of quantitative approaches around three central questions of neuroscience : how is the brain made up ? What functions and what calculations are performed ? By what mechanisms ?
The brain is a complex organ that performs sophisticated tasks in very precise ways. It is therefore often unexpected to be able to establish direct links between biochemistry and a given function of the brain.
Theoretical or computational neuroscience tries to fill this gap by suggesting possible mechanisms for perception, learning, memory, decision-making, motor control... In addition, more and more experimental data being more and more precise are collected every day. The simple fact of their profusion suggests the usefulness of theoretical principles that help "shaping" them for better understanding. The current precision of the experiments in turn allows detailed comparisons with proposed mathematical theories.
Aims : The purpose of the course is, firstly, to present a number of questions where a quantitative approach is relevant. Secondly, the course introduces mathematical methods necessary for the study of these questions, but also useful in other fields such as psychophysics, computer science, biophysics,... Finally, the course examines actual examples of problems whose understanding can benefit from a quantitative approach. Some examples of such questions are : how do neurons encode input information to the brain ? Does a single neuron perform a function or is it done by a batch of neurons ? How can vision be so precise ? How can we model learning and memory ? How does the brain generate its "output messages" like those that control muscles ?
Organisation : Typically a lecture followed by a TD. This teaching merges students from DEC, Physics and Biology Departments of the ENS.

Assessment

Presentation of a scientific article and written exam.

Teaching team

Jean-Pierre NADAL
Vincent HAKIM
Srdjan OSTOJIC
Boris BARBOUR
Alex CAYCO-GAJIC
Yves BOUBENEC
Elie ORIOL