Advanced Neural
Data Analysis and Neuroinformatics
ANDA-NI

Sept. 25 – Nov. 8, 2024

Registration open!

An interactive  school for improving
research data management and
electrophysiological data analysis

Learn advanced analysis techniques

Techniques to record neuronal data from populations of neurons are rapidly improving. Simultaneous recordings from hundreds of channels are possible while animals perform complex behavioral tasks. The analysis of such massive and complex data becomes increasingly challenging and intricate. The ANDA-NI school aims at teaching state-of-the-art analysis approaches for neuroelectrophysiological data in systems neuroscience.

Image: Denker, M., Grün, S., Wachtler, T., Scherberger, H., 2021. Neuroforum 27, 27–34. https://doi.org/10.1515/nf-2020-0041

Implement solutions for efficient data handling and sharing 

Likewise, the management of modern electrophysiology datasets becomes increasingly important to enable the synergistic effort of teams working jointly on this data. ANDA-NI provides an in-depth practical training on how to efficiently work with cellular and network-level electrophysiological data in a collaborative environment and in the implementation of workflows based on current neuroinformatics tools.  

How ANDA-NI works

ANDA-NI evolves the concepts of four ANDA schools held since 2017 by introducing practical aspects of neuroinformatics to the data analysis workflow. The school is addressed to excellent MSc and PhD students and experienced researchers who are excited to acquire skills in handling and analyzing neuroelectrophysiological data. The focus is on extracellular spike train recordings and local field potential recordings.

The school is comprised of a series of online preparatory lectures and an extended time period of instructed data curation, leading up to a week of hands-on, in-person experience in the analysis of real-world electrophysiological data. To inspire and guide analysis ideas, internationally renowned researchers will give lectures on statistical data analysis and data mining methods with accompanying exercises. For the practical work, students have the opportunity to bring their own example dataset, which they will learn to efficiently describe and share using neuroinformatics tools. During the school, students will then work in tandems on analyzing their datasets using the methods they have learned about in the lectures, defined by their own interests and ideas.

The initial online preparatory part of the school (Sept. 25–27, 2024) covering neuroinformatics topics is open to guests via a simplified application.

How to apply

ANDA-NI applications
Participants are required to have a strong interest in data analysis, a background in a mathematical or related field, knowledge of algebra, matrix operations, and statistics, and solid programming experience (preferably in Python).  

The application includes a motivation letter, an example Python code written by the applicant that is informative of their level of coding proficiency, and a short letter of support by their supervisor for MSc and PhD applicants.

In addition, students are required to identify a dataset they intend to bring to the school. During this school, students will learn to efficiently share this dataset among themselves, and later use these data to explore analysis methods in small teams during the in-person part of the school. For maximum benefit, we strongly encourage participants to bring a representative example of data they are working on (e.g., an example session) and to allow the sharing of this dataset among participants of the school, or optionally even publish of this dataset as part of a repository hosted on the G-Node data sharing infrastructure. Please ask for appropriate permissions before applying. Alternatively, we also welcome participants who bring publicly available, open datasets that are of interest to them. 

Download the registration form and send the filled form and any attachments to applications@andani.info.

Deadline: July 31, 2024

Virtual guests for the neuroinformatics lectures
Please apply for the virtual short track neuroinformatics school (Sept. 25–27, 2024) by sending an email with a short statement of motivation to applications@andani.info.

Deadline: July 31, 2024

Participation Fee

ANDA-NI applications
The fee for ANDA-NI participants is 900 Euro. During the in-person part of the school in November, this fee covers accommodation (Sunday-Saturday, 6 nights), lunches (Monday-Friday), dinners (Monday-Thursday), drinks and coffee. All other costs, e.g., travel to Jülich, must be paid by the participant.

A maximum number of 14 participants are accepted.

Virtual guests for the Neuroinformatics Curriculum
The event is free of charge for accepted guests (Sept. 25-27, 2024). A maximum number of 20 guests are accepted in addition to ANDA-NI participants.


Questions? Contact us at contact@andani.info.

Image: Forschungszentrum Jülich GmbH, Ralf-Uwe Limbach

Venue

The school will be held at the Jülich Research Center, Germany on Nov. 4-8, 2024. In addition, the school will consist of an initial virtual preparatory lecture series held Sept. 25-27, 2024. Students will be required to allocate an estimated equivalent of approximately 5 working days on preparing contributed datasets between Sept. 28-Nov. 3,  2024 under the supervision of school tutors.



Schedule

Sept. 24, 2024
Kick-off Meeting (online)

Sept. 25-27, 2024
Preparatory Neuroinformatics Curriculum (online)
Covers data representations for cellular and network-level electrophysiological data, git-based management of data, reproducibility, and sharing of data.

Sept. 28 - Nov. 3, 2024
Dataset Preparation Period (3-4 online meetings with tutors and faculty; total effort approx. 5 days)

Nov. 4-8, 2024
Project Week (in person)

Faculty

Michael Denker

Forschungszentrum Jülich
Jülich, Germany

Tools for the Analysis of Electrophysiology Data

Nicholas Del Grosso

iBehave iBOTS, University of Bonn
Bonn, Germany

Management of Neuroscience Data and Workflows

Sonja Grün

Forschungszentrum Jülich
Jülich, Germany

Analysis of higher-order correlation structures in brain activity

Thomas Wachtler

Ludwig-Maximilians-Universität München, Germany

Tools for Management of Electrophysiology Data

Hansjörg Scherberger

German Primate Center
 Göttingen, Germany

Analysis of Activity Data  in Primate Cortex

Martin Nawrot

University of Cologne
Cologne, Germany

Statistics and Variability of Activity Data

Andrea Brovelli

CNRS & Aix-Marseille Université
Marseille, France

Causality and Directionality Analysis

Byron Yu

Carnegie Mellon University
Pittsburgh, USA

Dimensionality Reduction

tba