Course overview
This course provides a foundation for new experimental neuroscientists. It is targeted at students beginning a PhD or researchers entering the field from another discipline. It should be considered a “prerequisite” for more advanced training courses in a specialized topic.
The course introduces the essentials of data acquisition/control, data analysis, and machine learning by guiding the 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
Voight-Kampff
Course directors
Adam Kampff
Course Director Voight Kampff, London, UK
Elena Dreosti
Co-Director University College London, UK
Instructors
– Peter Vincent (Sainsbury Wellcome Centre, London, UK)
– Spenser Wilson (Sainsbury Wellcome Centre, London, UK)
– Eirinn Mackay (Sainsbury Wellcome Centre)
– Virginia Rutten (The Gatstby Computational Neuroscience Unit)
What will you learn?
You will be building a robot without using any black boxes. The robot’s physical layout mimics the basic anatomy of a (vertebrate) brain, and as you gradually open this course’s 21 “boxes” your robot will evolve into an increasingly sophisticated machine. We thus call this robot the No-Black-Box-Bot or NB3.
The course is divided into three sections (following the anatomy of the brain): hindbrain (reflexes), midbrain (behaviour), and forebrain (intelligence?).
The online material can be found here.
Week 1: Measuring and Moving
Aims:
Students build a basic sensory-motor system (a Braitenberg vehicle) that seeks or avoids light and learn about fundamentals of electronics, sensors/actuators, and amplification.
Schedule:
Day 1: “The White Box” Toolkit, Electrons
Day 2: Magnets + Light, Sensors + Motors
Day 3: Semiconductors
Day 4: Amplifiers
Day 5: Reflexes, NB3 demos
Week 2: Computers and Programming
Aims:
Students extend their robot to make decisions based on sensory input and perform basic computations. With the addition of a microcontroller, the students will learn the fundamentals of computers and programming, and the robots will develop more complex behaviours.
Schedule:
Day 1: Decisions, Logic
Day 2: Data, Memory
Day 3: Computers
Day 4: Control
Day 5: Behaviour, NB3 demos
Week 3: Data Analysis and Machine learning
Aims:
Students add a computer and camera to their robot. They then learn how to use modern neural networks to create an “intelligent” visual system for their robot that can identify obstacles, rewards, and much more…
Schedule
Day 1: Hearing + Speech, Vision
Day 2: Learning, Intelligence?
Day 3: NB3 work, NB3 work
Day 4: NB3 work, NB3 work
Day 5: NB3 work, NB3 demos
Receive information
If you want more information about this NeuroKit, email info@cajal-training.org
