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This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.

Difficulty level: Intermediate
Duration: 56:49

This lesson outlines Neurodata Without Borders (NWB), a data standard for neurophysiology which provides neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data.

Difficulty level: Intermediate
Duration: 29:53
Speaker: : Oliver Ruebel

This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

Difficulty level: Intermediate
Duration: 17:08
Speaker: : Ari Ercole

This tutorial demonstrates how to use PyNN, a simulator-independent language for building neuronal network models, in conjunction with the neuromorphic hardware system SpiNNaker. 

Difficulty level: Intermediate
Duration: 25:49

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

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

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

Difficulty level: Intermediate
Duration: 12:50
Speaker: : Dan Goodman

This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.

Difficulty level: Intermediate
Duration: 0:40

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

Difficulty level: Intermediate
Duration: 1:23

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

Difficulty level: Intermediate
Duration: 1:20

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

Difficulty level: Intermediate
Duration: 1:08

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

Difficulty level: Intermediate
Duration: 1:18

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.

Difficulty level: Intermediate
Duration: 1:26

This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.

Difficulty level: Intermediate
Duration: 0:42

In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).

Difficulty level: Intermediate
Duration: 2:40