In this hands-on tutorial, Dr. Robert Guangyu Yang works through a number of coding exercises to see how RNNs can be easily used to study cognitive neuroscience questions, with a quick demonstration of how we can train and analyze RNNs on various cognitive neuroscience tasks. Familiarity of Python and basic knowledge of Pytorch are assumed.
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.
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
This lesson consists of a talk about the history and future of academic publishing and the need for transparency, as well as a live demo of an alpha version of NeuroLibre, a preprint server that goes beyond the PDF to complement research articles. This video was part of a virutal QBIN SciComm seminar.
This lecture covers the three big questions: What is the universe?, what is life?, and what is consciousness?
This lecture outlines various approaches to studying Mind, Brain, and Behavior.
This lecture covers the history of behaviorism and the ultimate challenge to behaviorism.
This lecture covers various learning theories.
An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.
How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.
In this lightning talk, you will learn about BrainGlobe, an initiative which exists to facilitate the development of interoperable Python-based tools for computational neuroanatomy.