This is a freely available online course on neuroscience for people with a machine learning background. The aim is to bring together these two fields that have a shared goal in understanding intelligent processes. Rather than pushing for “neuroscience-inspired” ideas in machine learning, the idea is to broaden the conceptions of both fields to incorporate elements of the other in the hope that this will lead to new, creative thinking.

This course contains sessions from the second day of INCF's Neuroinformatics Assembly 2022.

This course contains sessions from the first day of INCF's Neuroinformatics Assembly 2022.

This module provides an introduction to the motivation of deep learning and its history and inspiration.

This course consists of 12 lectures on the visual system and neural coding produced by the Allen Institute for Brain Science. The lectures cover broad neurophysiological concepts such as information theory and the mammalian visual system, as well as more specific topics such as cell types and their functions in the mammalian retina. 

The importance of Research Data Management in the conduct of open and reproducible science is better understood and technically supported than ever, and many of the underlying principles apply as much to everyday activities of a single researcher as to large-scale, multi-center open data sharing.

This course consists of one lesson and one tutorial, focusing on the neural connectivity measures derived from neuroimaging, specifically from methods like functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI). Additional tools such as tractography and parcellation are discussed in the context of brain connectivity and mental health. The tutorial leads participants through the computation of brain connectomes from fMRI data. 

The Neurodata Without Borders: Neurophysiology project (NWB, https://www.nwb.org/) is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy-barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.

This workshop provides an opportunity to explore the advanced tools and techniques for data sharing, analysis, visualization, and simulation.

This brief course consists of slides on data science and reproducibility issues from lectures given at Maastricht University. 

This course features tutorials on how to use Allen atlases and digital brain atlasing tools, including operational and user features of the Allen Mouse Brain Atlas, as well as the Allen Institute's 3D viewing tool, Brain Explorer®.

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

The landscape of scientific research is changing. Today’s researchers need to participate in large-scale collaborations, obtain and manage funding, share data, publish, and undertake knowledge translation activities in order to be successful. As per these increasing demands, Science Management is now a vital piece of the environment. This course consists of lectures presenting practical techniques, tools, and project management skills that participants can begin to implement.

Given the extreme interconnectedness of the human brain, studying any one cerebral area in isolation may lead to spurious results or incomplete, if not problematic, interpretations. This course introduces participants to the various spatial scales of neuroscience and the fundamentals of whole-brain modelling, used to generate a more thorough picture of brain activity.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

This couse is the opening module for the University of Toronto's Krembil Centre for Neuroinformatics' virtual learning series Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience. Lessons in this course introduce participants to the study of brain disorders, starting from elemental units like genes and neurons, eventually building up to whole-brain modelling and global activity patterns.

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and remaining barriers.

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.