Open source, Python based, behavioural experiment control.
pyControl is a system of open source hardware and software for controlling behavioural experiments, built around the Micropython microcontroller.
pyControl makes it easy to program complex behavioural tasks using a clean, intuitive, and flexible syntax for specifying tasks as state machines.
DANNCE (3-Dimensional Aligned Neural Network for Computational Ethology) is a convolutional neural network (CNN) that calculates the 3D positions of user-defined anatomical landmarks on behaving animals from videos taken at multiple angles.
Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages.
One of the biggest challenges in neuroscience is to understand how neural circuits in the brain process, encode, store, and retrieve information. Meeting this challenge requires tools capable of recording and manipulating the activity of intact neural networks in freely behaving animals.
Autopilot is a Python framework for performing complex, hardware-intensive behavioral experiments with swarms of networked Raspberry Pis. As a tool, it provides researchers with a toolkit of flexible modules to design experiments without rigid programming & API limitations.
World Wide Series Seminar FED3 is an open-source battery-powered device for home-cage training of mice in operant tasks. FED3 can be 3D printed and the control code is open-source and can be modified.
The advent of genetically encoded calcium indicators, along with surgical preparations such as thinned skulls or refractive index matched skulls, have enabled mesoscale cortical activity imaging in head-fixed mice. Such imaging studies have revealed complex patterns of coordinated activity across the cortex during spontaneous behaviors, goal-directed behavior, locomotion, motor learning,and perceptual decision making.
When performing canine operant conditioning studies, the delivery of the reward can be a limiting factor of the study. While there are a few commercially available options for automatically delivering rewards, they generally require manual input, such as using a remote control, in accordance with the experiment script.
Experiments aiming to understand sensory-motor systems, cognition and behavior necessitate training animals to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial.
lab.js makes online experimentation easy. It’s a free, open, online study builder and data collection framework for the behavioral, neuro- and cognitive sciences. Studies built with lab.js run in the laboratory as well as online, across a wide variety of data collection platforms, and provide excellent performance for all modes of stimulus presentation.