3.00 crédits
20.0 h + 10.0 h
Q2
Enseignants
Lee John; Missal Marcus (coordinateur(trice));
Langue
d'enseignement
d'enseignement
Préalables
Master in Biomedical Sciences, Neurosciences or equivalent background (Exact Sciences, Psychology, Applied Sciences).
Le(s) prérequis de cette Unité d’enseignement (UE) sont précisés à la fin de cette fiche, en regard des programmes/formations qui proposent cette UE.
Le(s) prérequis de cette Unité d’enseignement (UE) sont précisés à la fin de cette fiche, en regard des programmes/formations qui proposent cette UE.
Thèmes abordés
- Theoretical Neurosciences.
- Bayesian Approach.
- AI and Neurosciences.
- Synchrony and Oscillations
- Bayesian Approach.
- AI and Neurosciences.
- Synchrony and Oscillations
Acquis
d'apprentissage
d'apprentissage
A la fin de cette unité d’enseignement, l’étudiant est capable de : | |
| 1 | Read papers about neurocomputational models in neuroscience |
| Explain different types of models in computational neurosciences. |
|
Contenu
I. Introduction to Theoretical Neuroscience
- Goal: What is theoretical neuroscience? Its relationship to experimental neuroscience, mathematics, physics, and computer science.
- Key Topics: The role of theory (prediction, interpretation, synthesis). Levels of analysis (Marr's three levels: computational, algorithmic, implementation). Causality in Neurosciences.
- Introduce the fundamental building block and its electrical properties.
- The Hodgkin-Huxley Model (ionic currents, action potential generation).
- Integrate-and-Fire models and their variants (e.g., leaky I&F).
- How does the brain represent information? How can we read it out?
- Rate coding vs. Temporal coding.
- Predictive Coding (prediction error).
- Free energy principle.
- Bridging the gap between biological neural networks and Artificial Neural Networks (ANNs).
- Comparing ANNs and the brain (e.g., backpropagation vs. local learning rules).
- Convolutional Neural Networks (CNNs) as models for the visual cortex.
- Recurrent Neural Networks (RNNs) and sequence processing.
- Network rhythms and their functional role.
- Synchronization, binding problem.
- Coupled Oscillator Models (Kuramoto model).
- Different frequency bands (α,β,γ) and their hypothesized functions (e.g., γ oscillations in attention).
Méthodes d'enseignement
Ex-cathedra lessons, remotely if necessary. Group discussions. Article presentations by students.
Modes d'évaluation
des acquis des étudiants
des acquis des étudiants
Oral or written examination (questions with written development or multiple choices).
Ressources
en ligne
en ligne
https://moodleucl.uclouvain.be/course/view.php?id=9189
Support de cours
- https://moodleucl.uclouvain.be/course/view.php?id=9189
Faculté ou entité
en charge
en charge
