Summer school Banyuls-sur-Mer, 6-12 August 2023
“Advanced Computational Analysis for Behavioral
and Neurophysiological Recordings”

The recent years have seen the explosion of high-throughput neural recording methods: hundreds of individual neurons can now be monitored simultaneously using large-scale functional imaging or multisite electrode arrays. A comparable trend is observed in behavioral studies where new imaging technology produces high-resolution imaging of complex behaviors. 
In order to extract meaningful information from these high-dimensional datasets, neurobiologists need to develop and use robust computational methods. This Summer school aims at addressing this need by offering theoretical as well as direct practical exposure to computational tools used in modern neuroscience to interpret high dimensional neurophysiological and behavioral signals associated to complex behaviors.
Morning sessions will be dedicated to lectures covering the theoretical and experimental background required to understand the state-of-the-art computational techniques used in modern neuroscience. During the afternoons, these techniques will be effectively implemented during practical hands-on sessions, in which participants will learn to use them on actual data. Participants will choose in advance a specific dataset on which they will work for the entire workshop. Several datasets will be proposed to the students, comprising hippocampal electrophysiological recordings in rodents, whole-brain calcium imaging data in zebrafish larvae and behavioral data of freely moving animals.
This workshop will cover the following topics: behavioral tracking, spike sorting and spike inference, regression analyses and decoding, dimensionality reduction and clustering methods, time series analyses. It is mainly intended for PhD students and post-docs working on behavioral and/or neurophysiological data who are interested in using or developing these computational methods in their own project.

TEACHERS:
• Brice Bathellier (IdA, Institut Pasteur, Université de Paris, Inserm)
• Simona Cocco (LPENS, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris)
• Stéphanie Daumas (NPS, IBPS, Inserm, CNRS, Sorbonne Université)
• Georges Debrégeas (LJP, IBPS, CNRS, Sorbonne Université)
• Julien Fournier (NPS, IBPS, Inserm, CNRS, Sorbonne Université)
• Nicolas Gervasi (CIRB, College de France, Inserm)
• Benoît Girard (ISIR, CNRS, Sorbonne Université)
• Gabrielle Girardeau (IFM, Inserm, Sorbonne Université)
• Rémi Monasson (LPENS, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris)

PROGRAM:
Day 1: Behavioral analyses and data preprocessing 
Day 2: Regression analysis and decoding
Day 3: Dimensionality reduction and clustering methods
Day 4: Time series analysis
Day 5: Computational modeling, discussions and reporting

AUDIENCE
This workshop is meant for PhD students and post-doctoral fellows in Neuroscience who intend to use or develop computational methods to analyze neurophysiological and/or behavioral data. Applicants should have a minimal background in programming. A maximum of 20 participants will be selected.

VENUE
The school will take place at the Marine Biological Station of Banyuls-sur-Mer: https://www.obs-banyuls.fr/en/.
Information on how to get there can be found at: https://www.obs-banyuls.fr/en/host/how-to-get-here.html.

REGISTRATION
Please fill this form to apply. Application deadline April 15, 2023.
The registration fees are 100 euros, and cover housing and food.

ORGANIZATION
The school is co-organized by i-Bio and SCAI.
Organizers: Volker Bormuth (LJP, IBPS, Sorbonne University), Stephanie Daumas (NPS, IBPS, Sorbonne University), Georges Debregeas (LJP, IBPS, Sorbonne University), Gilles Fischer (i-Bio, IBPS, Sorbonne Universite), Julien Fournier (IBPS, NPS, Sorbonne Universite), Xavier Fresquet (SCAI, Sorbonne universite) Gabrielle Girardeau (IFM), Valerie Goguel (i-Bio, IBPS), Nora Roger (SCAI, Sorbonne universite)

Summer School Banyuls-sur-Mer, 25-29 July 2022,

“Artificial Intelligence for Biologists”

Overview
Very exciting new developments in artificial intelligence, particularly in deep learning, have achieved spectacular performances when applied to biological problems. For instance, the AlphaFold system produces protein 3D models based on their sequences, with an accuracy competitive with experimental structures, and this has greatly expanded proteomes structural coverage. Promising steps have also been taken for determining protein interacting partners and complex 3D structures, predicting the functional outcome of point mutations, and designing new proteins with desired shapes or functions. This summer school will offer an overview of the most recent deep neural network architectures and learning algorithms, and their application to protein-related problems. The participants will acquire theoretical knowledge and practical know-how on data representation, architecture design, and training/testing protocols. We will put a particular emphasis on the specific properties of protein sequences and structures, and on how these properties can be leveraged for improving the learning process. Proteins will be used as “case studies” to illustrate general issues associated with machine learning and the definition of meaningful representations in “digital” biology. The participants will be able to transfer the knowledge acquired during the school to other problems and objects (e.g. variant calling in DNA/RNA sequences from nanopore sequencing data, genotype-to-phenotype mapping…). The school will gather leading scientists coming from different backgrounds, namely biology, mathematics, computer science and physics, and working at the interface between artificial intelligence and biology. It is mainly intended for PhD students and post-doctoral fellows in biology and bioinformatics with some interest in using, understanding and developing machine learning methods. 

Teachers

  • Sophie Barbe, TBI, INSA – CNRS – INRAE 
  • Claire Boyer, LPSM, Sorbonne University
  • Tatiana Galochkina, DSIMB, INSERM – University of Paris 
  • Alessandra Carbone, LCQB, CNRS – Sorbonne University 
  • Jean-Christophe Gelly, DSIMB, INSERM – University of Paris 
  • Sergei Grudinin, LJK, CNRS
  • Elodie Laine, LCQB, CNRS – Sorbonne University

The mornings will be dedicated to lectures, and the afternoons to practical sessions. The first day will give a panorama of deep learning techniques, and an introduction to protein sequences and structures. The other days will deal with the prediction of protein structures, conformations and interactions, and protein design.

Audience
The school is open to PhD students and post-doctoral fellows in Biology or Bioinformatics who wish to integrate artificial intelligence techniques into their research. Candidates should have some (possibly light) background in programming and some basic knowledge on proteins. A maximum of 20 participants will be selected.

Venue
The school will take place at the Marine Biological Station of Banyuls-sur-Mer: https://www.obs-banyuls.fr/en/. Information on how to get there can be found at: https://www.obs-banyuls.fr/en/host/how-to-get-here.html.

Registration
Please fill this form to apply: https://forms.gle/UEastjG6Jsr2WDuo9.
Application deadline April 15, 2022.

The registration fees are 50 euros, and cover the housing and food expenses. Depending on the budget, we may also be able to provide financial support for travel expenses to some of the participants.

Organization
The school is co-organized by i-Bio and SCAI.
Organizers: Elodie Laine (executive chair, IBPS), Clément Carré (IBPS), Xavier Fresquet (SCAI), Valérie Goguel (i-Bio, IBPS), and Gilles Fischer (i-Bio, IBPS)

Sponsors
The school is sponsored by the GDR BIM (http://www.gdr-bim.cnrs.fr) and the Life Sciences Department of Sorbonne University.

For questions about the school, please contact: elodie.laine@sorbonne-universite.fr.

To download the announcement poster : click here