This lesson provides an overview of the database of Genotypes and Phenotypes (dbGaP), which was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in humans.
This lecture covers modeling the neuron in silicon, modeling vision and audition, and sensory fusion using a deep network.
This lesson gives an overview 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.
This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.
This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.
This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.
This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.
This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.
This lesson serves a primer on elementary algebra.
This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science.
In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.
This talk gives a primer on calculus, emphasizing its role in data science.
This lesson clarifies how calculus relates to optimization in a data science context.
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.
This lesson serves as a primer on the fundamental concepts underlying probability.
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.
This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.
This lesson was created by RootMath.
This lecture covers the description and characterization of an input-output relationship in a information-theoretic context.
This lesson is part 1 of 2 of a tutorial on statistical models for neural data.