Experimental Neuroscience Bootcamp
September 1st to 5th 2025
Application Deadline: 1 June 2025
COURSE OVERVIEW
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 the knowledge gaps of modern experimental technology. 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, concepts in neuroscience are introduced as nature’s solution to the challenges that participants encounter while designing and building their own intelligent system.
The unique integrated understanding of modern technology offered by this course has helped many scientists, with no prior knowledge, to use and design novel state-of-the-art neuroscience experiments.
WHAT WILL YOU LEARN?
By building an intelligent robot, you will learn:
basics of analog and digital electronics by building circuits for sensing the environment and controlling movement.
how simple digital circuits (logic gates, memory registers, etc.) can be assembled into a (programmable) computer.
how a modern computer’s “operating system” coordinates the execution of internal and external tasks, and how to communicate over a network.
how to work with images in a Python environment, detect features and learn about classical face detection.
how modern deep neural networks are applied in image processing by training your own network to allow your robot to identify it’s creator, you.
FACULTY
DIRECTORS
Adam Kampff
Course Director (Course Director
Voight Kampff, London, UK)
Elena Dreosti
Course Director (University College London, UK)
Andreas Kist
Course Director
(Department for AIBE, Erlangen, DE)
TEACHING ASSISTANTS
Every year we have an amazing group of Teaching Assistants who remotely help the students as they work on the course activities. Past TAs have been selected from several institutes around the world such as the Champalimaud Foundation (PT), Sainsbury Wellcome Centre (UK), and Neurogears (UK).
PROGRAMME
Day 1 – Sensors and Motors
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
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 Networks
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
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
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 network processing 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:
Learning: Train a neural network
Deployment: Deploy and run a deep neural network
Object detection: Find faces using a SSD (Single Shot Detector)
Object classification: Train a classifier to onyl identify one’s own face
APPLY
Applications are currently open!
Registration fee
Individual: £650 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).
Group: £650 for one person and one course kit + £350 for each additional person (without the course kit). Max group size is 3 students per kit.
Deadline: 1 June 2025
SPONSORS
This course has been kindly supported by the following sponsors and partners:
