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Advanced Class in Computational Neuroscience (ABCCN) 2023-24

General information :

Organizers : Jürgen Reingruber and David Holcman
Where ENS-Paris 46 rue d’Ulm 75005 Paris. 3 floor : Room 316

When Starting October 2023 for 3 months.


Starting date : 4th of October at the ENS at 5:15 PM for Master Student and 5pm for everybody for two hours

Every Wednesday 5:15pm-7:30pm.

On-line class Zoom link :

https://bbb.bio.ens.psl.eu/b/jur-yj7-fzc-rsb

Context
Understanding the brain multiscale structures, their interconnection, neuronal coding, learning and memory remain key challenges for modern neuroscience. Computational approaches based on physical modeling, multiscale simulations, mathematical analysis and data sorting, signal processing or machine learning have strongly impacted the research in this area and are crucial to reveal fundamental principles of brain functioning.

Goal
This interdisciplinary course will introduce Master, PhD students and postdocs to computational methods in neuroscience, and show their biological and clinical relevance. It will provide a welcoming and interactive environment for students to learn, understand, and discuss neuroscience with researchers working in this field.

  • Neuroscience : We will introduce fundamental topics in neuroscience : sensory input to the brain, dynamics at single neuron and network level, models applied to neuronal dysfunctions, plasticity mechanisms underlying learning and memory, analysis and interpretation of EEG data, brain-machine interface.
  • Methods : We will introduce computational methods to study brain functioning based on multi-scale modeling, simulations, data analysis and machine-leaning.
  • Themes:Chemical and mechanical signal transduction models, information theory and coding, electrodiffusion models, modeling calcium dynamics, neuronal network models, graph theory, time series analysis, machine learning and artificial neural networks.
  • Biological and clinical relevance : Vision restoration, epilepsy, anesthesia, Brain-Computer interface.
  • Assessment : Students will have the possibility to acquire a grade by performing a short project or presenting a paper.

Background : Computational Biology, Engineering, Physics, Applied Mathematics, Computer Science

Introductory Class

1. Brain function and clinical issues, examples (October 4) Nathalie Kubis (Hôpital Lariboisière)

Introduction-PDF and Link to Recording
Kubis-PDF and Link to Recording

Sensory transduction

2. Modelling hair cells as sensory receptors and mechanical amplifiers for hearing (October 11) Pascal Martin (Institut Curie)

3. Modelling the transduction of light into an electrical signal by rod and cone photoreceptors in the retina (October 18) Jürgen Reingruber (ENS)

4. Neural coding in the retina and vision restoration (October 25)
Olivier Marre (Institut de la Vision)

Fundamental brain constituents

5. Electrodiffusion model for synapses and dendritic spines (November 2) Thibault Lagache (Institut Pasteur)

6. Modelling Neuron-Glial interactions and synaptic plasticity (November 8) David Holcman (ENS)

Neural Networks

7. Modelling and analysis of neural circuit dynamics : emergent rhythms and their interplay. (November 15) Boris Gutkin (ENS)

8. Modelling spatial memory and navigation (November 22) Denis Sheynikhovich (Sorbonne University)

9. Mesocopic models : from neural circuits to large-scale activity, in normal and pathological states (November 29) Alain Destexhe (University Paris-Saclay)

10. Big data analysis applied to behavioral neuroscience (December 6) Gisella Vetere (ESPCI)

EEG analysis and Brain-Computer Interfaces

11. Analyse des signaux électrophysiologiques : des cycles lents aux oscillations rapides (December 13) Michel Le Van Quyen (Sorbonne University)

12. Signal processing and machine learning for Brain-Computer Interfaces (December 20)
Theodore Papadopoulo (INRIA Nice)