The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the maturity and accessibility of virtualization technologies has made it feasible to run the same analysis environments on platforms ranging from single laptop computers up to high-performance computing networks.

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.

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.

Bayesian inference (using prior knowledge to generate more accurate predictions about future events or outcomes) has become increasingly applied to the fields of neuroscience and neuroinformatics. In this course, participants are taught how Bayesian statistics may be used to build cognitive models of processes like learning or perception. This course also offers theoretical and practical instruction on dynamic causal modeling as applied to fMRI and EEG data.

This course consists of two workshops which focus on the need for reproducibility in science, particularly under the umbrella roadmap of FAIR scienctific principles. The tutorials also provide an introduction to some of the most commonly used open-source scientific tools, including Git, GitHub, Google Colab, Binder, Docker, and the programming languages Python and R. 

Data science relies on several important aspects of mathematics. In this course, you'll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems.

This course outlines how versioning code, data, and analysis software is crucially important to rigorous and open neuroscience workflows that maximize reproducibility and minimize errors.Version control systems, code-capable notebooks, and virtualization containers such as Git, Jupyter, and Docker, respectively, have become essential tools in data science.

This course consists of several introductory lectures on different aspects of biochemical models. The lectures cover topics such as stability analysis of neural models, oscillations and bursting, and weakly coupled oscillators. You will learn about modeling various scales and properties of neural mechanisms, from firing-rate models of single neurons to pattern generation in visual system hallucinations. 

This course consists of several lightning talks from the second day of INCF's Neuroinformatics Assembly 2023. Covering a wide range of topics, these brief talks provide snapshots of various neuroinformatic efforts such as brain-computer interface standards, dealing with multimodal animal MRI datasets, distributed data management, and several more. 

This course corresponds to the second session of INCF's Neuroinformatics Assembly 2023. This series of talks continues a discussion of FAIR principles from the first session, with a greater emphasis on brain data (humans and animals) atlases for data analysis and integation. 

This lecture series is presented by NeuroTechEU, an alliance between eight European universities with the goal to build a trans-European network of excellence in brain research and technologies. By following along with this series, participants will learn about the history of cognitive science and the development of the field in a sociocultural context, as well as its trajectory into the future with the advent of artificial intelligence and neural network development.

This course explores ethical and social issues that have arisen, and continue to arise, from the rapid research development in neuroscience, medicine, and ICT. Lectures focus on key ethical issues contained in the HBP – such as the ethics of robotics, dual use, ICT ethical issues, big data and individual privacy, and the use of animals in research.

A series of short explanations of the basic equations underlying computational neuroscience.

This course contains videos, lectures, and hands-on tutorials as part of INCF's Neuroinformatics Assembly 2023 workshop on developing robust and reproducible research workflows to foster greater collaborative efforts in neuroscience.

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

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

VIEW THE PROGRAM

This course consists of several lightning talks from the second day of INCF's Neuroinformatics Assembly 2023. Covering a wide range of topics, these brief talks provide snapshots of various neuroinformatic efforts such as brain-computer interface standards, dealing with multimodal animal MRI datasets, distributed data management, and several more. 

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.

Most neuroscience journals request authors to make their data publicly available in appropriate repositories. The requirements and policies put forward by journals vary, and the services provided for different types of data also differ considerably across repositories.

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