UE946 - MOD 202 - Computational neuroscience methods

Type d'UE
Méthodologie
Disciplines
Psychologie et sciences cognitives
Page web
https://cogmaster.ens.psl.eu/en/program/m1-program-13570 
Langues
anglais
Mots-clés
Méthodes et techniques des sciences sociales Sciences cognitives
Aires culturelles
-

The aim of this practical course is to learn to implement computational models of neural systems, and to perform basic analyses of neural data. The students will use Python to work on several projects that include:

1. simulating a spiking neuron,

2. simulating a network of neurons,

3. estimating a receptive field from neural data,

4. estimating the information contained in neural responses,

5. simulating an animal’s behaviour during reinforcement learning.

Introduction to computational modelling in Python

Behavioral modelling: Rescorla-Wagner rule

Behavioral modelling: instrumental conditioning

Behavioral modelling: drift-diffusion model

Neural variability and spike-train statistics

Integrate-and-fire neurons

Firing-rate models

Hopfield networks

  • Sciences cognitives – M1/S2
    Suivi et validation – semestriel hebdomadaire = 4 ECTS
    MCC – contrôle continu
  • Srdjan Ostojic [référent·e]   directeur de recherche, CNRS /
Contacts additionnels
cogmaster@psl.eu
Informations pratiques

The complete syllabus of the course is available on the Cogmaster's website. For any information, please contact the secretariat of the Cogmaster.

Registration procedure (external students) : https://cogmaster.ens.psl.eu/en/students/external-students-13501

Direction de travaux des étudiants
-
Réception des candidats
-
Pré-requis

Basics of programming with Python.

  • Autre lieu Paris
    ENS 29 rue d'Ulm 75005 Paris (salle à préciser)
    2nd semestre / hebdomadaire, vendredi 14:00-17:00
    du 5 février 2021 au 21 mai 2021


Intervenant·e·s


  • Srdjan Ostojic [référent·e]   directeur de recherche, CNRS /

Planning


  • Autre lieu Paris
    ENS 29 rue d'Ulm 75005 Paris (salle à préciser)
    2nd semestre / hebdomadaire, vendredi 14:00-17:00
    du 5 février 2021 au 21 mai 2021


Description


Type d'UE
Méthodologie
Disciplines
Psychologie et sciences cognitives
Page web
https://cogmaster.ens.psl.eu/en/program/m1-program-13570 
Langues
anglais
Mots-clés
Méthodes et techniques des sciences sociales Sciences cognitives
Aires culturelles
-

The aim of this practical course is to learn to implement computational models of neural systems, and to perform basic analyses of neural data. The students will use Python to work on several projects that include:

1. simulating a spiking neuron,

2. simulating a network of neurons,

3. estimating a receptive field from neural data,

4. estimating the information contained in neural responses,

5. simulating an animal’s behaviour during reinforcement learning.

Introduction to computational modelling in Python

Behavioral modelling: Rescorla-Wagner rule

Behavioral modelling: instrumental conditioning

Behavioral modelling: drift-diffusion model

Neural variability and spike-train statistics

Integrate-and-fire neurons

Firing-rate models

Hopfield networks


Master


  • Sciences cognitives – M1/S2
    Suivi et validation – semestriel hebdomadaire = 4 ECTS
    MCC – contrôle continu

Renseignements


Contacts additionnels
cogmaster@psl.eu
Informations pratiques

The complete syllabus of the course is available on the Cogmaster's website. For any information, please contact the secretariat of the Cogmaster.

Registration procedure (external students) : https://cogmaster.ens.psl.eu/en/students/external-students-13501

Direction de travaux des étudiants
-
Réception des candidats
-
Pré-requis

Basics of programming with Python.