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

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.

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.

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

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices. 

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system. 

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

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.

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.

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. 

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. 

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. 

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. 

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

This lecture focuses on ontologies for clinical neurosciences.

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course. 

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.

This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.