Introduction to modeling in neuroscience and cognition

Teacher	face to face hours	working hours	ects
Bruno Cessac (Inria) Rodrigo Cofre (Inria) PhD Student for tutorials	45	90	9

Description

The nervous system is composed of multiple subsystems interacting in parallel, across different spatial scales—from molecules to the whole brain—and temporal scales—from milliseconds to years. Understanding these subsystems and their interactions is essential to interpret experimental data (multi-electrode recordings, optical imaging, EEG, MEG, etc.) and to explain how cognitive functions emerge from cortical dynamics.
Because the nervous system constantly interacts with a changing environment, we also aim to understand how the multi-scale dynamics of neural assemblies adapt so quickly and reliably. This requires appropriate theoretical models and analytical tools, grounded in experimental neuroscience.
This course provides a practical and operational introduction to brain dynamics at different scales: from neurons and synapses, to neural networks, to neural masses, up to the emergence of cognitive processes. Biological, physical, mathematical, computational, and cognitive aspects will be addressed in parallel. Acquisition techniques (multi-electrodes, optical imaging, EEG, MEG, etc.) will be presented alongside experimental protocols linking behavior to in vivo recordings during cognitive tasks.
 

Learning Outcomes

In this course, the students will learn how to combine fundamental knowledge in physics, biology, mathematics and computer science, to better understand the multi-scale dynamics of neural assemblies. They will also develop their critical thinking: How can I check if a model or a simulation of neural assemblies is correct ? How to reproduce experimental results ? Are the physical units coherent ? How to make testable predictions ?
 

Requirements

Students are expected to attend classes, complete exercises, actively participate in discussions, and read the assigned materials.