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
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.
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.
Download printable leaflet.
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 EXTENDED -- New deadline: August 21, 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 EXTENDED -- New deadline: August 21, 2024
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
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.
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)
Forschungszentrum Jülich
Jülich, Germany
Tools for the Analysis of Electrophysiology Data
iBehave iBOTS, University of Bonn
Bonn, Germany
Management of Neuroscience Data and Workflows
Forschungszentrum Jülich
Jülich, Germany
Analysis of higher-order correlation structures in brain activity
Ludwig-Maximilians-Universität München, Germany
Tools for Management of Electrophysiology Data
German Primate Center
Göttingen, Germany
Analysis of Activity Data in Primate Cortex
University of Cologne
Cologne, Germany
Statistics and Variability of Activity Data
CNRS & Aix-Marseille Université
Marseille, France
Causality and Directionality Analysis
Carnegie Mellon University
Pittsburgh, USA
Dimensionality Reduction
University of Bremen
Bremen, Germany
Spectral Signal Analysis