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 consists of a series of webinars organized by the International Neuroethics Society on various neuroethics topics.
Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience. FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible, Interoperable and Reusable. But FAIR is not a specification; it leaves many of the specifics up to individual scientific disciplines to define.
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.
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.
Presented by the Neuroscience Information Framework (NIF), this series consists of several lectures characterizing cutting-edge, open-source software platforms and computational tools for neuroscientists. This course offers detailed descriptions of various neuroinformatic resources such as cloud-computing services, web-based annotation tools, genome browsers, and platforms for designing and building biophysically detailed models of neurons and neural ensembles.
Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.
This course consists of a three-part session from the second day of INCF's Neuroinformatics Assembly 2023. The lessons describe various on-going efforts within the fields of neuroinformatics and clinical neuroscience to adjust to the increasingly vast volumes of brain data being collected and stored.
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.
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.
As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration.
Sessions from the INCF Neuroinformatics Assembly 2022 day 1.
Course designed for advanced learners interested in understanding the foundations of Machine Learning in Python.
General: The course consists of 15 lectures (ca. 1-2 hours each) and 15 exercise sheets (for ca. 6 hours of programming each).
Institution: High-Performance Computing and Analytics Lab, University of Bonn
Sessions from the INCF Neuroinformatics Assembly 2022 day 2.
This course provides introductory and refresher lessons for a range of concepts and methods useful in the field of neuroscience and neuroinformatics.
Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives.
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.
Course designed for advanced learners interested in understanding the foundations of Machine Learning in Python.
General: The course consists of 15 lectures (ca. 1-2 hours each) and 15 exercise sheets (for ca. 6 hours of programming each).
Institution: High-Performance Computing and Analytics Lab, University of Bonn
This course corresponds to the third session of talks given at INCF's Neuroinformatics Assembly 2023. In this session, the talks revolve around the idea of cross-platform data integration, discussing processes and solutions for rapidly developing an integrated workflow across independent systems for the US BRAIN Initiative Cell Census.