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Interacting with neural circuits

Course overview

Understanding the links between activity in neural circuits and behavior is a fundamental problem in neuroscience. Attacking this problem requires detailed information about the cell types in neural circuits and their connectivity, and recording the spatiotemporal patterns of activity in the intact brain during behaviour. Furthermore, probing causal relationships between cellular and circuit-level processes and behaviour requires perturbation of specific elements of the circuit in a temporally and spatially precise manner.

This course will highlight the new anatomical, genetic, optical, electrophysiological, optogenetic, and pharmacogenetic approaches that are available for addressing these challenges. The faculty will discuss tool development through to their implementation in diverse model systems, including mice and zebrafish. Students will learn the potential and limitations of these techniques, allowing them to both design and interpret experiments correctly.

Course directors

Michael Hausser

Course Director

Susana Lima

Course Director

Tiago Branco

Course Director

Executive director

Pedro Garcia da Silva

Course Executive Director

Keynote Speakers


Stan Heinze – Lund University, Sweden
Chris Xu
Cornell University, USA
Michael Orger – Champalimaud Foundation, Portugal
Constanze Lenschow
– Magdeburg University, Germany
Ana João Rodrigues
– ICVS, Minho University, Portugal
Bob Datta
– Harvard University, USA
Marta Moita
– Champalimaud Foundation, Portugal
Botond Roska – Institute of Molecular and Clinical Ophthalmology Basel, Switzerland
Tobias Rose – University of Bonn Medical Center, Germany
Darcy Peterka
– Columbia University, USA
Vanessa Ruta –
Rockefeller University, USA
Eugenia ChiappeChampalimaud Foundation, Portugal
Isaac Bianco
University College of London, UK
Christine Constantinople
– New York University, USA
Nicolò Accanto
– Institut de la Vision, France
Carsen Stringer
– HHMI Janelia, USA
Greg Jefferis
– MRC Laboratory of Molecular Biology, UK
Nick Steinmetz
– University of Washington, USA
Michael Brecht
– Bernstein Center for Computational Neuroscience, Germany

Course content

The course combines a lecture series featuring top speakers from around the world with a practical “hands-on” introduction to the latest methods for probing neural circuits, using drosophila, zebrafish, and (transgenic) mice. The course will focus on anatomy and connectivity, recording and manipulation, and the relation between circuits and behavior. During the course, each student will carry out a ‘mini-project’, executed under the guidance and supervision of experienced researchers and teaching assistants.

For more information on the course programme, you can visit the past course website.

Champalimaud Centre for the Unknown, Portugal

The Champalimaud Foundation is a private, non-profit organization, established in 2005 and dedicated to research excellence in biomedical science. Completed in 2010, the Champalimaud Centre for the Unknown is a state-of-the-art centre that houses the Champalimaud Clinical Centre and the Champalimaud Research, with its three parallel programs – the Champalimaud Neuroscience Programme, the Physiology and Cancer Programme, and the Experimental Clinical Research Programme.
Initially focused on a system and circuit approach to brain function and behavior, the Centre expanded to incorporate molecular and cell biological expertise. The Centre comprises 26 research groups (circa 400 researchers) leading independent curiosity-based research.

The Centre provides Facilities dedicated for Training, some in their entirety, for use by the CAJAL Advanced Neuroscience Training Programme. These include the Teaching Laboratory, a fully equipped open lab space for 20-30 students that can be dynamically reconfigured to support a full range of neuroscience courses. It also overlooks, via floor to ceiling windows, a tropical garden and the river. The experimental spaces include: Imaging Lab: A dark-room containing a full size optical table is used for advanced imaging setups (two-photon microscopy, SPIM, etc.) and custom (course-designed) optical systems.


Fee : 3.950 € (includes tuition fee, accommodation and meals)

Applications closed on 31 January 2023

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 pro- cess, depending on the course.


Supported by a gift from the Simons Foundation

Experimental Neuroscience Bootcamp 2023

Applications are closed but you can express your interest in this course, by using the form above. We will contact you when the applications re-open.

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

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: 500€ 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: 500for one person and one course kit + 150€ per additional person (without the course kit)

Applications are closed but you can express your interest in this course, by using the form above. We will contact you when the applications re-open.

To receive more information about this NeuroKit, email


Supported by a gift from the Simons Foundation

Connectomics from micro- to meso- and macro-scales

Course overview

The biological factors shaping the synaptic connectivity of neuronal circuits are complex and multifaceted, depending on cell types, functional activity, homeostasis, and more. Mapping brain wiring at the level of both local circuits and across brain-wide projections is a key aspect of understanding how nervous systems develop, learn, process information and generate behaviour. Recent advances in molecular biology, tissue processing, computational methods, and microscopy have enabled a revolution in understanding structural connectivity with cellular and synaptic resolution. Large-scale electron microscopy volumes provide nanometer-scale maps of anatomy and connectivity of whole invertebrate brains and millimetre-scale regions of vertebrate brains, while light-microscopic methods can highlight genetically defined connections and enable brain-wide reconstruction of neurons. Together, these complementary approaches yield powerful insight into the neuroanatomy and connectivity of the nervous system with single-cell resolution.

This course will provide students with a broad introduction to contemporary methods of studying neuronal connectivity with lectures from experts in the field. It also provides practical project-based instruction in experimental methods of circuit tracing and reconstruction with light microscopy (light sheet or 2-photon), as well as the computational analysis of rich electron microscopy connectomes in flies and mice. Students will consider the strengths and limitations of different techniques and how they can be used to address key problems in circuit neuroscience.

Course directors

Gregory Jefferis

Course Director

MRC LMB and University of Cambridge, UK

Jinny Kim

Course Director

Korea Institute of Science and Technology, Korea

Nicolas Renier

Course Director

Paris Brain Institute, France

Allen Institute of Brain Science, US

Keynote Speakers

Jae-Byum Chang – KAIST, South Korea
Christel Genoud – University of Lausanne, Switzerland
Moritz Hemlstaedter – Max Plank Institute for Brain Research, Germany
Valentin Nägerl – University of Bordeaux, France
Alexandra Pacureanu – European Synchrotron Radiation Facility, France
Hiroki Ueda – Laboratory for Synthetic Biology, Japan
Claire Wyart – ICM Institute for Brain and Spinal Cord, France
Johannes Kohl – The Crick Institute, UK
Gwyneth Card – Columbia University, USA

Instructors / Teaching Assistants

Alba Vieites Prado – Universitad de Santiago de Compostela, Spain
Fabian Voigt – Harvard University, USA
Faustine Ginoux – Paris Brain Institute, France
Forrest Collman – Allen Brain Institute, USA
Kathi Eichler – Leipzig University, Germany
Martin Carbo Tano – Paris Brain Institute, France
Philipp Schlegel – University of Cambridge, UK
Sahil Loomba – Max Planck Institute for Brain Research, Germany
Thomas Topilko – Gubra, Inc

Course content

Light Microscopy and functional, molecular methods:

– Choice of labelling strategy: use of specific cre lines (for instance, the GENSAT project); finding specific markers for cell populations; using viral vectors and intersectional genetics (Dual or triple injections, transsynaptic tracing, Tango system, mGRASP, etc.).

– Tissue preparation for imaging: tissue clearing: choice of methods, considerations for the resolution needed and type of molecular labelling (Ueda, Renier); expansion microscopy methods: when to use them, and which iteration (Jae-Byum Chang)

Imaging strategy: use of scanning microscopes: confocal or 2p, in intact samples or using serial sectioning; use of light sheet microscopy: commercial systems (eg. Miltenyi’s Blaze or Zeiss Z7), and custom systems (Mesospim).

Analysis of imaging data: use of neuron mapping pipelines for whole brain data obtained from light sheet microscopy or from sections (eg. ClearMap, TrailMap, WholeBrain, etc). (Ueda, Renier); se of virtual reality-assisted tools for single neuron reconstructions from 3D datasets (eg. SyGlass, Vision4D…).

Electron microscopy synaptic connectomics:

At the end of this course, the students will be familiar with all of the steps that go into producing and analysing large scale, synaptic resolution EM connectomics datasets, summarised as below. Detailed analysis projects tailored by student interest will use public datasets and open source tools in which their directors and their colleagues are experts. These include the microns mouse cortical cubic millimetre dataset ( and fly CNS datasets including the hemibrain, flywire.


  • EM imaging for connectomics (theory, image analysis)
  • X-ray imaging for connectomics (theory, image analysis)
  • EM connectomics data analysis (detailed hands on coverage for latest public whole brain fly and mouse cortex datasets; other organisms pending)
  • In vivo calcium imaging
  • In vivo 2-photon microscopy
  • Light sheet microscopy
  • Tissue Clearing
  • Expansion Microscopy
  • Brain mapping of cleared tissue (image analysis, commercial and academic softwares)


  1. Mapping of axonal projections with light sheet microscopy in the mouse brain
  2. Assembly of a Mesospim microscope for whole brain mapping with tissue clearing
  3. Brain mapping of cellular markers with HCR-fish, tissue clearing and light sheet microscopy
  4. In vivo recording of activity and connectivity in the Zebrafish larva
  5. Inference of information flow in Zebrafish larva using calcium imaging and Granger Causality
  6. Microscale connectomic analysis of mammalian connectomics data
  7. Morphological and connectivity analysis of the MICrONs mouse visual cortex dataset
  8. Student interest-led project(s) leveraging public mammalian connectomics data
  9. Whole brain circuit analysis using larval / adult Drosophila connectomes
  10. Student interest-led projects using public Drosophila connectomes
  11. Comparative connectomics: within species using multiple Drosophila datasets or across evolution using multiple public EM connectomes.

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 : 3.950 € (includes tuition fee, accommodation and meals)

Applications closed 17 April 2023

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 pro- cess, depending on the course.