This collection contains the the four sessions of talks and one series of lightning talks which took place virtually at INCF's Neuroinformatics Assembly 2023. This year's Assembly was themed around "Transparency in FAIR Neuroinformatics". The material in this collection therefore caters to two main groups:
This collection offers theoretical and practical instruction on neuroinformatic approaches to the research and care associated with mental health disorders. Particular emphasis is placed on the various spatial scales of neuroscientific investigation, as well as computational methods for integrating health-related data across domains.
The Essentials of Neuroscience With MATLAB course was developed to provide advanced undergraduates and early graduate students with a basic familiarity with MATLAB programming with an opportunity to deepen their expertise in neuroscience data analysis using MATLAB. Each module covers a range of specific data processing, analysis, and visualization skills that neuroscientists often need.
The INCF Assembly is a unique venue where neuroscience standards developers, infrastructure providers, and software developers have the opportunity to interact with the research community to share the latest advancements in neuroinformatics. INCF Assembly 2022 was hosted on the Gather platform. This collection contains recordings of the sessions from the Assembly 2022.
The objective of the 2021 INCF Neuroinformatics Assembly was to provide a forum in which the neuroscience community can learn about the latest advancements in the application of the FAIR Guiding Principles in neuroscience and attend tutorials on the latest tools, methods, and neuroinformatics approaches that promote open, FAIR, and citable neuroscience.
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: introduction to data science or a graduate-level machine learning course.
The CAJAL Advanced Neuroscience Training Programme was founded by The Federation of European Neuroscience Societies (FENS) and the International Brain Research Organization (IBRO) to establish a high-level neuroscience training hub in Europe.
These courses will introduce the basics of powerful machine learning techniques and the elements of traditional statistical approaches provide foundational knowledge for multivariate analyses.
This collection looks to introduce neuroscience trainees to many of the basic tools and techniques essential for most computationally intensive neuroscience research environments.
This collection of courses and lessons intends to provide resources for standards and best practices in Open Science, Publishing, Ethics, and more.
The collection aims to provide several categories of skills and knowledge relevant to the practice of Data Science & Neuroinformatics in and open neuroscience environment.
This collection aims to integrate into the INCF Training Space open training materials originating from Canadian neuroscience research labs forming part of the CONP network, as well as make use of the expertise of CONP members, partners, and Scholars to help curate and expand existing materials.…
The objective of this collection of tutorials is to provide an introduction to neuroscientist interested in converting their neurophysiology data to NWB, a unified, extensible, open-source data format for cellular-based neurophysiology data, as well as to present the major tools that are NWB-enabled.
Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. The collection is intended for participant populations ranging from undergraduates to faculty in academic settings as well as industry professionals. In addition to teaching the technical details of computational methods, the curriculum is centered on modern neuroscience concepts taught by leading professors along with explicit…
The Virtual Brain takes a network approach on the largest scale: By manipulating network parameters, in particular the brain’s connectivity, The Virtual Brain simulates its behavior as it is commonly observed in clinical scanners (e.g. EEG, MEG, fMRI).