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.

In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.

Lecture on the most important concepts in software engineering

This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network. 

Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.

Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.

Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.

The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.

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

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

Action potentials, and biophysics of voltage-gated ion channels.

Voltage-gating kinetics of sodium and potassium channels.

The ionic basis of the action potential, including the Hodgkin Huxley model.