Probability and Statistics in Neural Systems

  • Science and Technology
Probability and Statistics in Neural Systems

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Objectifs

Initial training / Executive Education Program / Accessible for resumption of studies /
Diplôme d'université
2 months
Campus SophiaTech - Site des Lucioles
English

Details

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Introduction

The main objective of this University Diploma (DU) is to meet a need of post-graduate training (master's degree, doctorate, post-doctorate, professional) who wish to better understand and to make the best use of random simulations and statistical analysis of neuro-cognition data.
This Diploma is part of the refresher module "Bootcamp" of the Master of Science "Modeling for Neuronal and Cognitive Systems". 

EPU les Lucioles, 1645 Route des Lucioles, 06410 Biot

Patricia REYNAUD-BOURET, DR CNRS, Laboratoire Jean-Alexandre Dieudonné

Admission

Prerequisite

Graduate level, Undergraduate level

This training is open to any applicant with an undergraduate level (Bac+3). Some prior knowledge in mathematics (having taken courses in statistics in the past) is mandatory. 

English level B2/C1 is required. 

Students must have a certain ease with numerical tools so that the exchanges are more about the content rather than about the computer part (backup, installation etc.).

Conditions of applications

Applications are open until the 15th of June. 

You can submit your application online via ecandidat::       APPLY HERE

Program

This D.U. provides general training in probability and statistics with models and data from neuroscience or cognition: law of large numbers, central limit theorem and approximate confidence intervals, simulations in R, statistical tests, interpretation of p-values, etc.

Content: 
  • Random modeling: event, probability distributions (basic examples: Bernoulli, Binomial, Poisson, Gaussian, Exponential...), independence;
  • Law of large numbers, central limit theorem and approximate confidence intervals (with simulations in R);
  • Conditional laws and Markov chain to model the refractory period of neurons (with simulations in R) ;
  • Statistical tests and p-values (on very basic psychological experiments);
  • A discussion of reproducible research, p-hacking and p-curve;
  • Correlation, tests of independence and linear regression (with applications in R);
  • How do we check that it is Gaussian? What do we do if it is not? (Some non-parametric tests, spike train analysis and coincidence analysis) ;
  • Interpretation of p-values, multiple tests (Bonferroni, Benjamini-Hochberg) ;
  • Linear Gaussian models and variable selections (with applications in R);
  • Principal component analysis.

NB: Students will be coached for programming in R. The only prerequisite is to install R-studio on their computer. 

Full time

The training is concentrated on a short period of time but very intense. Classes are organized at least 2 to 3 times per week (3 hours per class). The total amount represents 42 hours of teaching. 

Partly attending

Students can benefit either from face-to-face training or participate online if necessary. 

Fully remote

Courses can be recorded and be released on Moodle. Teaching assistants are available to reply to questions in the chat and help on the assignements. 

What's next ?

Year of highschool graduation

Diplôme Universitaire

Target activities / attested skills

This short course allows students to obtain proven and essential skills for their research mission in biology or psychology and to improve their skills in random modeling and data analysis.