https://github.com/esther-poniatowski/2223_ulmm2_thneuro
TDs sheets and corrections
https://github.com/esther-poniatowski/2223_ulmm2_thneuro
Last synced: 4 months ago
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TDs sheets and corrections
- Host: GitHub
- URL: https://github.com/esther-poniatowski/2223_ulmm2_thneuro
- Owner: esther-poniatowski
- Created: 2022-09-18T03:35:09.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-31T04:51:23.000Z (over 1 year ago)
- Last Synced: 2023-12-31T05:25:40.487Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 20.4 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Theoretical Neuroscience TDs
*Master in Cognitive Sciences (Cogmaster), ENS, 2023-2024.*
## Practical information
:information_source: [General public information](http://www.phys.ens.fr/~nadal/Cours/TheoreticalNeuroscience/)
**TD assistant** Esther Poniatowski
:e-mail: [email protected]
:warning: **PROCEDURE TO ACCESS THE MOODLE**
To get access to the moodle of the course - if this is not yet the case -:
① Connect with your institutional email address to https://moodle.u-paris.fr/
② Send a mail to [email protected] telling that this has been done, and we will be able to give you access to the moodle of the course.
:warning: **REGISTRATION FOR EXTERNAL STUDENTS**
Non Cogmaster students have to register with the Cogmaster.
See here [http://www.phys.ens.fr/~nadal/Cours/TheoreticalNeuroscience/](https://cogmaster.ens.psl.eu/en/application/external-students-13501)
:warning: **SHEET FOR GROUPS COMPOSITION (FINAL ARTICLE PRESENTATION)**
[http://www.phys.ens.fr/~nadal/Cours/TheoreticalNeuroscience/](https://docs.google.com/spreadsheets/d/1CX9o1aqD8ap8mC7MRpgAWRIjUYAe3W1ikEC6eNUryZs/edit?usp=sharing)
## News
> Dear all,
> First of all, please have a look at the important information above :warning:.
> Second, from now on, the relevant content in each TD (essentially, the parts tackled during the session) will be indicated in the table below (Programm).
> Third, I uploaded the corrections of TD2.
> I stay at your disposal for any other question or remark.
> I wish you a good week-end.
## Programm
| Date | TD | Topics | Content Tackled during the session |
|----------|:-------------:|------|------|
| 23-09-21 | TD1 | Models of Neurons I - Leaky-Integrate-and-Fire model | 2. Models of Point Neurons, 3.1 & 3.2 Leaky-Integrate-and-Fire model. |
| 23-09-28 | TD2 | Models of Neurons II - Generalized Integrate-and-Fire models (QIF, EIF, adaptative models) | 1.2. Quadratic Integrate-and-Fire model. 2.2. Adaptive Exponential Integrate-and-Fire model.
| 23-10-05 | TD3 | Synapses & Dendrites | 4.1 Receptors kinetics & Post-synaptic current, Comparing alpha functions and Markov kinetics |
| 23-10-12 | TD4 | Models of Neurons III - Conductance-based models (minimal models, Hodgkin-Huxley model, Futz-Hugh Nagumo model) | 3.1 FitzHugh-Nagumo mode, Local analysis |
| 23-10-19 | TD5 | Balanced Networks | 1. Poissonian spike trains, 3. Stochastic integration of synaptic inputs (q.14)|
| 23-10-26 | TD6 | Rate Models | 1. Input current & Uniform state, 2. Description through order parameters, 3. Bumpy perturbation (q.5) |
| 23-11-16 | TD7 | Learning I - Unsupervised Learning (Hebb's rule) | 1. Modeling a binocular neuron, 2.1 Standard Hebbian learning |
| 23-11-23 | TD8 | Learning II - Supervised Learning (Perceptron) | 1. & 2. |
| 23-12-07 | TD9 | Learning III - Reinforcement Learning | 1. Markov Decision Process, 3.1 Analytical study – Model-free agent performing Temporal-Difference Learning |
| 22-12-14 | TD10 | Neuronal Coding | 1.1 & 1.2 Mutual Information, 2.1 Fisher Information (Distance between probability distributions) |
## Survey
Please fill the following questionnaire after the first TD session.
[Link to the form](https://forms.gle/ydGEfeTznT2y4udc8)