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.

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 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 lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity. 

This lesson describes the fundamentals of genomics, from central dogma to design and implementation of GWAS, to the computation, analysis, and interpretation of polygenic risk scores. 

This lesson contains the slides (pptx) of a lecture discussing the necessary concepts and tools for taking into account population stratification and admixture in the context of genome-wide association studies (GWAS). The free-access software Tractor and its advantages in GWAS are also discussed. 

This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.