3.00 credits
20.0 h + 10.0 h
Q2
Teacher(s)
Lee John; Missal Marcus (coordinator);
Language
English
> French-friendly
> French-friendly
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Content
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).
Teaching methods
Ex-cathedra lessons, remotely if necessary. Group discussions. Article presentations by students.
Evaluation methods
Oral or written examination (questions with written development or multiple choices).
Online resources
https://moodleucl.uclouvain.be/course/view.php?id=9189
Teaching materials
- https://moodleucl.uclouvain.be/course/view.php?id=9189
Faculty or entity
