The probability of a hypothesis, given data.

Why math is useful in data science.

Why statistics are useful for data science.

Statistics is exploring data.

Graphical data exploration

Numerical data exploration

Simple description of statistical data.

Basics of hypothesis testing.

Enabling neuroscience research using high performance computing

This lecture covers structured data, databases, federating neuroscience-relevant databases, ontologies. 

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

This lecture was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

This lecture was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

This lecture covers describing and characterizing an input-output relationship.

Part 1 of 2 of a tutorial on statistical models for neural data

Part 2 of 2 of a tutorial on statistical models for neural data.

From the retina to the superior colliculus, the lateral geniculate nucleus into primary visual cortex and beyond, this lecture gives a tour of the mammalian visual system highlighting the Nobel-prize winning discoveries of Hubel & Wiesel.

From Universal Turing Machines to McCulloch-Pitts and Hopfield associative memory networks, this lecture explains what is meant by computation.

Ion channels and the movement of ions across the cell membrane.

The "connectome" is a term, coined in the past decade, that has been used to describe more than one phenomenon in neuroscience. This lecture explains the basics of structural connections at the micro-, meso- and macroscopic scales.