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 video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes. 

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

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 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 lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

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 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.

The Allen Mouse Brain Atlas is a genome-wide, high-resolution atlas of gene expression throughout the adult mouse brain. This tutorial describes the basic search and navigation features of the Allen Mouse Brain Atlas.

The Allen Developing Mouse Brain Atlas is a detailed atlas of gene expression across mouse brain development. This tutorial describes the basic search and navigation features of the Allen Developing Mouse Brain Atlas.

This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain, as well as to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.

This module covers some basic anatomy such as the brain’s major divisions (brainstem, cerebellum, cerebrum), the cerebral lobes (frontal, temporal, parietal, and occipital), the central and peripheral nervous systems, theories of cognition, and brain orientation terms.

This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.