Course overview

Modern neuroscience relies on the combination of multiple technologies to record precise measurements of neural activity and behaviour. Commercially available software for sampling and controlling data acquisition is often too expensive, closed to modification and incompatible with this growing complexity, requiring experimenters to constantly patch together diverse pieces of software.

This course will introduce the basics of the Bonsai programming language, a high-performance, easy to use, and flexible visual environment for designing closed-loop neuroscience experiments combining physiology and behaviour data. 

This language has allowed scientists with no previous programming experience to quickly develop and scale-up experimental rigs, and can be used to integrate new open-source hardware and software.


Course Teaser

What will you learn?

By the end of the course you will be able to use Bonsai software to:

– create data acquisition and processing pipelines for video and visual stimulation.
– control behavioral task states and run your closed-loop experiments.
– collect data from cameras, microphones, Arduino boards, electrophysioology data, etc.
– achieve precise synchronization of independent data streams
– understand Bonsai workflows, marble diagrams and observable sequence.

The online material will be soon found here.


Gonçalo Lopes

Course Director

NeuroGEARS, London, UK​


Joao Frazao Champalimaud Research, Lisbon, PT

Nicholas Guilbeault – University of Toronto, CA

Course sponsors


Day 1 – Introduction to Bonsai

  • Introduction to Bonsai. What is visual reactive programming.

  • How to measure almost anything with Bonsai (from quantities to bytes).

  • How to control almost anything with Bonsai (from bytes to effects).

  • How to measure/control multiple things at the same time with one computer.

  • Demos and applications: a whirlwind tour of Bonsai.

Day 2 – Cameras, tracking, controllers

  • Measuring behavior using a video.

  • Recording a real-time video from multiple cameras.

  • Real-time tracking of colored objects, moving objects and contrasting objects..

  • Measuring behavior using voltages and Arduino.

  • Data synchronization. What frame did the light turn on?

Day 3 – Real-time closed-loop assays

  • What can we learn from closed-loop experiments?

  • Conditional effects. Triggering a stimulus based on video activity.

  • Continuous feedback. Modulate stimulus intensity with speed or distance.

  • Feedback stabilization. Record video centered around a moving object.

  • Measuring closed-loop latency.

Day 4 – Operant behavior tasks

  • Modeling trial sequences: states, events, and side-effects.

  • Driving state transitions with external inputs.

  • Choice, timeouts and conditional logic: the basic building blocks of reaction time, Go/No-Go and 2AFC tasks.

  • Combining real-time and non real-time logic for good measure.

  • Student project brainstorming

Day 5 – Visual stimulation and beyond

  • Interactive visual environments using BonVision.

  • Machine learning for markerless pose estimation using DeepLabCut.

  • Multi-animal tracking and body part feature extraction with BonZeb.

  • Student project presentation.

  • Where to next.


Fee : 300 € (includes lectures and kit)

Please apply through the course online application form. 

Deadline 20 December 2020

To receive more information about this NeuroKit, email