Overview of the content for Day 1 of this course.
This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.
This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.
In this lesson, users will learn about human brain signals as measured by electroencephalography (EEG), as well as associated neural signatures such as steady state visually evoked potentials (SSVEPs) and alpha oscillations.
Best practices: the tips and tricks on how to get your Miniscope to work and how to get your experiments off the ground.
This talk delves into challenges and opportunities of Miniscope design, seeking the optimal balance between scale and function.
Attendees of this talk will learn aobut computational imaging systems and associated pipelines, as well as open-source software solutions supporting miniscope use.
This talk covers the present state and future directions of calcium imaging data analysis, particularly in the context of one-photon vs two-photon approaches.
In this talk, results from rodent experimentation using in vivo imaging are presented, demonstrating how the monitoring of neural ensembles may reveal patterns of learning during spatial tasks.
How to start processing the raw imaging data generated with a Miniscope, including developing a usable pipeline and demoing the Minion pipeline.
The direction of miniature microscopes, including both MetaCell and other groups.
Overview of the content for Day 2 of this course.
Summary and closing remarks for this three-day course.
This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.
This lecture covers infrared LED oblique illumination for studying neuronal circuits in in vitro block-preparations of the spinal cord and brain stem.
This lecture covers the application of diffusion MRI for clinical and preclinical studies.
This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This lesson gives a description of the BrainHealth Databank, a repository of many types of health-related data, whose aim is to accelerate research, improve care, and to help better understand and diagnose mental illness, as well as develop new treatments and prevention strategies.
This lesson corresponds to slides 46-78 of the PDF below.
This lecture covers the history of behaviorism and the ultimate challenge to behaviorism.