Category Archives: 2022

Neuroepigenetics: writing, reading and erasing the epigenome

Course overview

This course is a theoretical and practical training on the recently emerged field of neuroepigenetics. It will provide an overview of the current concepts and knowledge on the nature and functions of the epigenome in the nervous system, its modes of regulation and its link to brain health and disease. It will combine lectures and hands-on projects to learn about state-of-the-art approaches and methodologies to study how the epigenome is established and modulated by behaviour in rodents and invertebrates, what machinery is involved and what is its causal relationship to functions. It will include methods in behaviour, epigenetics, (epi)genome editing, molecular and cell biology, -omics and bioinformatics.

This course wishes to foster the development of neuroepigenetics as a key discipline in the neurosciences, and train and educate young researchers expected to contribute to the field in the near future. It is intended for PhD students and early career postdocs who wish to acquire good bases and broad knowledge of the field.

Course partner

Course directors

Karine Merienne

Course Director

CNRS – LNCA, University of Strasbourg, France

Angel Barco


Neurosciences Institute (UMH-CSIC), Spain

André Fischer


German Center for Neurodegenerative diseases (DZNE), Germany

Institute of Informatics, University of Warsaw, Poland

Keynote Speakers

Elisabeth Binder – Max Planck Institute of Psychiatry, Munich, Germany
Anne-Laurence Boutillier – LNCA, Strasbourg, France
Goncalo Castelo Branco – Karolinska Institute, Stockholm, Sweden
Johannes Gräff – EPFL, Lausanne, Swiss
Elisabeth Heller – Penn Epigenetics Institute, Philadelphia, USA
Denes Hnisz – Max Planck Institute for Molecular Genetics, Berlin, Germany
Aleksandra Pekowska – Dioscuri Center of Chromatin Biology and Epigenomics, Poland


Rafael Alcala Vida – Instituto de Neurociencias UMH-CSIC, Spain
Mykhailo Batiuk – EPFL, Lausanne, Swiss
Davide Coda – EPFL, Lausanne, Swiss
Charles Decraene – LNCA, Strasbourg, France
Beatriz del Blanco Pablos – Instituto de Neurociencias UMH-CSIC, Spain
Aleksander Jankowski – Institute of informatics, Warsaw, Poland
Lalit Kaurani – DZNE, Göttingen, Germany
Marta Kullis – IDIBABS, Barcelona, Spain
Stéphanie Legras – IGBMC, Strasbourg, France
Jose Lopez Atalaya – Instituto de Neurociencias UMH-CSIC, Spain
Pierre-Eric Lutz – INCI, Strasbourg, France
Magdalena Machnicka – Institute of informatics, Warsaw, Poland
Isabel Paiva – LNCA, Strasbourg, France
Jose Sanchez-Mut – Instituto de Neurociencias UMH-CSIC, Spain
Tanya Vavouri – Pujol Research Institute, Badalona, Spain

Bordeaux School of Neuroscience, France

The Bordeaux School of Neuroscience is part of Bordeaux Neurocampus, the Neuroscience Department of the University of Bordeaux. Christophe Mulle, its current director, founded it in 2015. Throughout the year, renowned scientists, promising young researchers and many students from any geographical horizon come to the School.
The school works on this principle: training in neuroscience research through experimental practice, within the framework of a real research laboratory.

Their dedicated laboratory (500m2), available for about 20 trainees, is equipped with a wet lab, an in vitro and in vivo electrophysiology room, IT facilities, a standard cellular imaging room, an animal facility equipped for behavior studies and surgery and catering/meeting spaces. They also have access to high-level core facilities within the University of Bordeaux. They offer their services to international training teams who wish to organize courses in all fields of neuroscience thanks to a dedicated staff for the full logistics (travels, accommodation, on-site catering, social events) and administration and 2 scientific managers in support of the experimentation.


Fee : 3.500 € (includes tuition fee, accommodation and meals)

Application deadline: 25 July 2022, 23:59 CEST

The CAJAL programme offers 4 stipends per course (waived registration fee, not including travel expenses). Please apply through the course online application form. In order to identify candidates in real need of a stipend, any grant applicant is encouraged to first request funds from their lab, institution or government.

Kindly note that if you benefited from a Cajal stipend in the past, you are no longer eligible to receive this kind of funding. However other types of funding (such as partial travel grants from sponsors) might be made available after the participants selection process, depending on the course.

Our partner, ERA-NET NEURON, will also offer grants to participants who are part of an ERA-NET Neuron research group. Please indicate in the application form if you are a member of the network.

Experimental Neuroscience Bootcamp 1122

This is a Cajal NeuroKit course that combines online lectures about fundamentals and advanced neuroscience topics, with hands-on and physical experiments.
Researchers from all over the world can participate because the course material is sent home in box, and contains all the tools needed to follow the online course.

This course is now at its second edition

Course overview

This course provides a fundamental foundation in the modern techniques of experimental neuroscience. It introduces the essentials of sensors, motor control, microcontrollers, programming, data analysis, and machine learning by guiding students through the “hands on” construction of an increasingly capable robot.

In parallel, related concepts in neuroscience are introduced as nature’s solution to the challenges students encounter while designing and building their own intelligent system.

Course Partners

What will you learn?

The techniques of experimental neuroscience advance at an incredible pace. They incorporate developments from many different fields, requiring new researchers to acquire a broad range of skills and expertise (from building electronic hardware to designing optical systems to training deep neural networks). This overwhelming task encourages students to move quickly, but often by skipping over some essential underlying knowledge.

This course was designed to fill-in these knowledge gaps.

By building a robot, you will learn both how the individual technologies work and how to combine them together into a complete system. It is this broad-but-integrated understanding of modern technology that will help students of this course design novel state-of-the-art neuroscience experiments.

Course directors

Adam Kampff

Course Director
Voight Kampff, London, UK

Andreas Kist

Course Director
Department for Artificial Intelligence in Biomedical Engineering (AIBE), Erlangen, Germany

Elena Dreosti

University College London, UK


Day 1 – Sensors and Motors

What will you learn?

You will learn the basics of analog and digital electronics by building circuits for sensing the environment and controlling movement. These circuits will be used to construct the foundation of your course robot; a Braitenberg Vehicle that uses simple “algorithms” to generate surprisingly complex behaviour.

Topics and Tasks:

  • Electronics (voltage, resistors, Ohm’s law): Build a voltage divider

  • Sensing (light-dependent resistors, thermistors): Build a light/temperature sensor

  • Movement (electro-magentism, DC motors, gears): Mount and spin your motors

  • Amplifying (transistors, op-amps): Build a light-controlled motor

  • Basic Behaviour: Build a Braitenberg Vehicle

Day 2: Microcontrollers and Programming

What will you learn?

You will learn how simple digital circuits (logic gates, memory registers, etc.) can be assembled into a (programmable) computer. You will then attach a microcontroller to your course robot, connect it to sensors and motors, and begin to write programs that extend your robot’s behavioural ability.

Topics and Tasks:

  • Logic and Memory: Build a logic circuit and a flip-flop

  • Processors: Setup a microcontroller and attach inputs and outputs

  • Programming: Program a microcontroller (control flow, timers, digital IO, analog IO)

  • Intermediate behaviour: Design a state machine to control your course robot

Day 3: Computers and Programming

What will you learn?

You will learn how a modern computer’s “operating system” (Linux) coordinates the execution of internal and external tasks, and how to communicate over a network (using WiFi). You will then use Python to write a “remote-control” system for your course robot by developing your own communication protocol between your robot’s linux computer and microcontroller.

Topics and Tasks:

  • Operating Systems: Setup a Linux computer (Raspberry Pi)

  • Networking: Remotely access a computer (SSH via WiFi)

  • Programming: Program a Linux computer (Python)

  • Advanced behaviour: Build a remote control robot

Day 4: (Machine) Vision

What will you learn?

You will learn how grayscale and color images emerge and how to work with them in a Python environment. By mounting a camera on your robot, you can live-stream the images to your computer. You will then use background subtraction and thresholding to program an image-based motion detector. You will use image moments to detect and follow a moving light source, and learn about “classical” face detection.

Topics and Tasks:

  • Images: Open, modify, and save images

  • Camera: Attach and stream a camera image

  • Image processing: Determine differences in images

  • Pattern recognition: Extract features from images

Day 5: (Machine) Learning

What will you learn?

You will learn about modern deep neural networks and how they are applied in image processing. You will extend the intelligence for your robot, by adding a neural accelerator to the robot. We will deploy a deep neural network for face detection and compare it to the “classical” face detector. Ultimately, you will create and train your own deep neural network that will allow your robot to identify it’s creator, you.

Topics and Tasks:

  • Inference: Implement a neural accelerator (Google Coral USB EdgeTPU)

  • Deployment: Deploy and run a deep neural network

  • Object detection: Finding faces using a deep neural network (Single Shot Detector)

  • Object classification: Train a deep neural network to identify one’s own face (TF/Keras)

The course will be held from 14:00 to 18:00 CET


Registration fee: 450€ per person (includes shipping of the course kit, pre-recorded and live lectures before and during the course, full attendance to the course, and course certificate).

Registration fee for a group: 450for one person and one course kit + 150€ per additional person (without the course kit)

Application closed on 26 July 2021

To receive more information about this NeuroKit, email