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.

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. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

This lecture covers computational principles that growth cones employ to detect and respond to environmental chemotactic gradients, focusing particularly on growth-cone shape dynamics.

In this lecture you will learn that in developing mouse somatosensory cortex, endogenous Btbd3 translocate to the cell nucleus in response to neuronal activity and oriente primary dendrites toward active axons in the barrel hollow.

In this presentation, the speaker describes some of their recent efforts to characterize the transcriptome of the developing human brain, and and introduction to the BrainSpan project.

This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics. 

This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes. 

In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte. 

This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

This video will document the process of creating a pipeline rule for batch processing on brainlife.

This lesson describes how DataLad allows you to track and mange both your data and analysis code, thereby facilitating reliable, reproducible, and shareable research.

This lecture discusses the challenges of protecting hospital data.

This lecture discusses differential privacy and synthetic data in the context of medical data sharing in clinical neurosciences.

This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses. 

This tutorial covers the fundamentals of collaborating with Git and GitHub.

This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.