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 visualizing extracellular neurotransmitter dynamics

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

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. 

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication? 

This lecture consists of the second half of the introduction to signal transduction, here focusing on cell receptors and signalling cascades.

In this lesson, you will learn about GABAergic interneurons and local inhibition on the circuit level.

This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.

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. 

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.

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.

In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI). 

This video demonstrates each required step for preprocessing T1w anatomical data in brainlife.io.

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

This lecture provides an introduction to the principal of anatomical organization of neural systems in the human brain and spinal cord that mediate sensation, integrate signals, and motivate behavior.