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. 

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.

How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.

In an overview of the structure of the mammalian neocortex, this lecture explains how the mammalian cortex is organized in a hierarchy, describing the columnar principle and canonical microcircuits

The retina has 60 different types of neurons. What are their functions? This lecture explores the definition of cell types and their functions in the mammalian retina.