Master in Cognitive Sciences (Cogmaster), ENS, 2023-2024.
TD assistant Esther Poniatowski
📧 eponiatowski@clipper.ens.psl.eu
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 jean-pierre.nadal@phys.ens.fr telling that this has been done, and we will be able to give you access to the moodle of the course.
Non Cogmaster students have to register with the Cogmaster.
See here http://www.phys.ens.fr/~nadal/Cours/TheoreticalNeuroscience/
http://www.phys.ens.fr/~nadal/Cours/TheoreticalNeuroscience/
Dear all,
First of all, please have a look at the important information above⚠️ .
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.
| 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) |
Please fill the following questionnaire after the first TD session.
Link to the form