This course covers the concepts of recurrent and convolutional nets (theory and practice), natural signals properties and the convolution, and recurrent neural networks (vanilla and gated, LSTM).

Ethical conduct of science, good governance of data, and accelerated translation to the clinic are key to high-calibre open neuroscience.  Everyday practitioners of science must be sensitized to a range of ethical considerations in their research, some having especially to do with open data-sharing. The lessons included in this course introduce a number of these topics and end with concrete guidance for participant consent and de-identification of data.

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.

The lecture series focuses on current trends in modern techniques in neuroscience. Inspiring scientists from the NeurotechEU Alliance will give an overview of the latest advances and developments.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

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In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

This course consists of three lessons, each corresponding to a lightning talk given at the first day of INCF's Neuroinformatics Assembly 2023. By following along these brief talks, you will hear about topics such as open source tools for computer vision, tools for the integration of various MRI dataset formats, as well as international data governance. 

This course introduces researchers to the Brain Imaging Data Structure (BIDS), the official community standard for organizing and sharing PET data. BIDS simplifies collaboration, streamlines analysis, and ensures your research remains future-proof by enabling compatibility with an ever-growing ecosystem of open datasets and community-developed tools.

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 tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like machine learning and artificial intelligence. While there is great potential for innovation and improvement in the clinical space thanks to AI development, lecturers in this course advocate for a greater emphasis on human-centric care, calling for algorithm design which takes the full intersectionality of individuals into account.

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.

A number of programming languages are ubiquitous in modern neuroscience and are key to the competence, freedom, and creativity necessary in neuroscience research. This course offers lectures on the fundamentals of data science and specific neuroinformatic tools used in the investigation of brain data. Attendees of this course will be learn about the programming languages Python, R, and MATLAB, as well as their associated packages and software environments. 

Most approaches within computational neuroscience simulate systems, brain networks, local circuits, as they are now. In recent years, homeostatic regulation has been characterized and modeled; however, for understanding diseases that have their origin in genetic defects that emerge at later age, it is important to understand how these defects interact with developmental processes that occur earlier and last longer that the typical period considered for homeostatic studies.

This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are led through the basics of genome-wide association studies (GWAS), genotypes, and polygenic risk scores, as well as novel concepts and tools for more sophisticated consideration of population stratification in GWAS.

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

This course provides introductory and refresher lessons for a range of concepts and methods useful in the field of neuroscience and neuroinformatics. 

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of presentations introduces the most popular platform for computational notebooks, Project Jupyter, as well as other resources like Binder and NeuroLibre. 

As technological improvements continue to facilitate innovations in the mental health space, researchers and clinicians are faced with novel opportunities and challenges regarding study design, diagnoses, treatments, and follow-up care. This course includes a lecture outlining these new developments, as well as a workshop which introduces users to Synapse, an open-source platform for collaborative data analysis. 

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.