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This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate
Duration: 1:09:33
Speaker: : Sean Hill

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity. 

Difficulty level: Intermediate
Duration: 1:16:10
Speaker: : John Griffiths

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

Difficulty level: Intermediate
Duration: 1:28:14

This talk gives a brief overview of current efforts to collect and share the Brain Reference Architecture (BRA) data involved in the construction of a whole-brain architecture that assigns functions to major brain organs. 

Difficulty level: Beginner
Duration: 4:02

This brief talk discusses the idea that music, as a naturalistic stimulus, offers a window into higher cognition and various levels of neural architecture.    

Difficulty level: Beginner
Duration: 4:04
Speaker: : Sarah Faber

In this short talk you will learn about The Neural System Laboratory, which aims to develop and implement new technologies for analysis of brain architecture, connectivity, and brain-wide gene and molecular level organization.

Difficulty level: Beginner
Duration: 8:38
Speaker: : Trygve Leergard

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks. 

Difficulty level: Intermediate
Duration: 6:00
Speaker: : Marcus Ghosh

This lesson goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems. 

Difficulty level: Intermediate
Duration: 12:52
Speaker: : Dan Goodman

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

Difficulty level: Intermediate
Duration: 50:44
Speaker: : Caterina Gratton

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec
Course:

Neuronify is an educational tool meant to create intuition for how neurons and neural networks behave. You can use it to combine neurons with different connections, just like the ones we have in our brain, and explore how changes on single cells lead to behavioral changes in important networks. Neuronify is based on an integrate-and-fire model of neurons. This is one of the simplest models of neurons that exist. It focuses on the spike timing of a neuron and ignores the details of the action potential dynamics. These neurons are modeled as simple RC circuits. When the membrane potential is above a certain threshold, a spike is generated and the voltage is reset to its resting potential. This spike then signals other neurons through its synapses.

Neuronify aims to provide a low entry point to simulation-based neuroscience.

Difficulty level: Beginner
Duration: 01:25
Speaker: : Neuronify

This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.

Difficulty level: Beginner
Duration: 2:53
Speaker: : Barton Poulson

This lesson serves a primer on elementary algebra.

Difficulty level: Beginner
Duration: 3:03
Speaker: : Barton Poulson

This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science. 

Difficulty level: Beginner
Duration: 5:38
Speaker: : Barton Poulson

In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.

Difficulty level: Beginner
Duration: 5:24
Speaker: : Barton Poulson

This talk gives a primer on calculus, emphasizing its role in data science.

Difficulty level: Beginner
Duration: 4:17
Speaker: : Barton Poulson

This lesson clarifies how calculus relates to optimization in a data science context. 

Difficulty level: Beginner
Duration: 8:43
Speaker: : Barton Poulson

This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.

Difficulty level: Beginner
Duration: 5:19
Speaker: : Barton Poulson

This lesson serves as a primer on the fundamental concepts underlying probability. 

Difficulty level: Beginner
Duration: 7:33
Speaker: : Barton Poulson

Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

Difficulty level: Beginner
Duration: 1:00:07
Speaker: : Shawn Grooms