This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.
This lesson explains the fundamental principles of neuronal communication, such as neuronal spiking, membrane potentials, and cellular excitability, and how these electrophysiological features of the brain may be modelled and simulated digitally.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.
This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.
In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs.
This lecture covers the ethical implications of the use of pharmaceuticals to enhance brain functions and was part of the Neuro Day Workshop held by the NeuroSchool of Aix Marseille University.
This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.
This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.
In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them.
This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.
This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.
This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.
This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.
This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.
This session discussed the secret life of your dataset metadata: the ways in which, for many years to come, it will work non-stop to foster the visibility, reach, and impact of your work. We explored how metadata will help your dataset travel through the global research infrastructure, and how data repositories and discovery services can use this metadata to help launch your dataset into the world.
This lesson provides information on developing data management plans (DMPs), including an overview of how DMPs contribute to effective research efforts, as well as specific development resources and DMP examples.
In this session, participants will take an in-depth look at the newly launched DMP Assistant 2.0, including all of its enhanced key features for both end-users and institutional administrators, as well as a brief look at the future of the platform.