How to start Computational Neuroscience
Disclaimer: I didn’t own any of the contents and it all belongs to the rightful owner credited/ mentioned/ referenced at the end of the post.
Purpose of the study track is to provide a convenient and guided starting point to acquire the knowledge and skills required for open neuroscience.
Brief introduction
Research in the neurosciences is becoming ever more demanding of a variety of sophisticated technical skills and computational competence, especially when one factors in the objective of making this science reproducible, open, and FAIR.
In collaboration with the INCF, the Canadian Open Neuroscience Platform (CONP) is assembling a curated set of international content that aims to provide guidance through the increasingly complex landscape of skills and tools required for open neuroscience research. Such initiatives are key to facilitating the acquisition of the skills and knowledge comprising open-science workflows (from ‘open-by-design’ experimental conception, through reproducible analysis, to safe data sharing). This is a living collection, with many materials to be added and updated still.
The CONP is funded by a Brain Canada Platform Support Grant Competition Award, as well as funds and in-kind support from sponsor organizations. Please visit the CONP and Brain Canada websites linked below for more information.
The track is divided into 3 seperate parts:
- Data Science - Tools of the Trade:
This collection looks to introduce neuroscience trainees to many of the basic tools and techniques essential for most computationally intensive neuroscience research environments. - Techniques on Statistics and Machine Learning:
These courses will introduce the basics of powerful machine learning techniques and the elements of traditional statistical approaches provide foundational knowledge for multivariate analyses. - Standard & Best Practices:
This collection of courses and lessons intends to provide resources for standards and best practices in Open Science, Publishing, Ethics, and more.
Data Science - Tools of the Trade
Table of contents
- Conceptual background & refreshers
- Review of modelling
- Flash math refresher
- Models of neural function
- Overview of brain-imaging techniques
- Programming essentials
- Basics required for navigating command-line environments
- Python
- R
- Matlab/Octave
- Notebooks
- For teaching and learning
- As part of an everyday, scientific workflow
- As a complement to standard PDF publications
- Versioning and containerization
- Overview
- Reproducibility
- Local execution
- Data Management, Repositories & Search Engines
- The importance and utility of Research Data Management
- Sources and reuse of neuroscience data
- High-performance computing
- Case studies
- CLI environments (tools, scheduling, etc.)
- GUI environments
Conceptual background & refreshers
Statistics and Machine Learning
Table of contents
- GLM, regression models, and latent variables
- Refresher for regressions models and GLM
- Addition of different noise distributions and advanced models
- Logistic regression
- Latent variables
- Machine learning
- Conceptual overview
- Hands-on application of simple machine learning to neuroscience data
- Advanced models
- Deep learning
- Caveats in deep learning applications to neuroscience
- Statistical software
- scikit-learn
- nilearn
- JASP
Standard & Best Practices
Table of contents
- Open Science: Practices and Policies
- The Open Science Training Handbook
- Standards for Project Management and Organization
- Support Your Research With Data Management Planning!
- Ethics and Governance
- Publishing (Still under development)
A few more things:
- From Neuromatch Academy 2025: How to Build Data Pipelines for Neuroscience & AI: https://youtu.be/PpDZgFyUlMQ
References
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