This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.
This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.
This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.
This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized.
This lecture highlights the importance of correct annotation and assignment of location, and updated atlas resources to avoid errors in navigation and data interpretation.
We are at the exciting technological stage where it has become feasible to represent the anatomy of an entire human brain at the cellular level. This lecture discusses how neuroanatomy in the 21st Century has become an effort towards the virtualization and standardization of brain tissue.
This lecture covers essential features of digital brain models for neuroinformatics, particularly NeuroMaps.
This presentation covers the neuroinformatics tools and techniques used and their relationship to neuroanatomy for the Allen Institute's atlases of the mouse, developing mouse, and mouse connectional atlas.
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 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 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 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 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 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.