This lecture covers an introduction to connectomics, and image processing tools for the study of connectomics.
This lecture covers acquisition techniques, the physics of MRI, diffusion imaging, prediction using fMRI.
Optical imaging offers a look inside the working brain. This lecture takes a look at orientation and ocular dominance columns in the visual cortex, and shows how they can be viewed with calcium imaging.
Functional imaging has led to the discovery of a plethora of visual cortical regions. This lecture introduces functional imaging techniques and their teachings about the visual cortex.
The Brain Imaging Data Structure (BIDS) is a standard prescribing a formal way to name and organize MRI data and metadata in a file system that simplifies communication and collaboration between users and enables easier data validation and software development through using consistent paths and naming for data files.
The Neuroimaging Data Model (NIDM) is a collection of specification documents that define extensions the W3C PROV standard for the domain of human brain mapping. NIDM uses provenance information as means to link components from different stages of the scientific research process from dataset descriptors and computational workflow, to derived data and publication.
Longitudinal Online Research and Imaging System (LORIS) is a web-based data and project management software for neuroimaging research studies. It is an open source framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.
This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
An agent for reproducible neuroimaging
This lecture was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.
Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.
Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.
This lecture will highlight our current understanding and recent developments in the field of neurodegenerative disease research, as well as the future of diagnostics and treatment of neurodegenerative diseases
Neuroethics has been described as containing at least two components - the neuroscience of ethics and the ethics of neuroscience. The first involves neuroscientific theories, research, and neuro-imaging focused on how the brain arrives at moral decisions and actions, which challenge existing descriptive theories of how humans develop moral thinking and make ethical decisions. The second, ethics of neuroscience, involves applying normative theories about what is right, good and fair to ethical questions raised by neuroscientific research and new technologies, such as how to balance the public benefit of “big data” neuroscience while protecting individual privacy and norms of informed consent.
The HBP as an ICT flagship project crucially relies on ICT and will contribute important input into the development of new computing principles and artefacts. Individuals working on the HBP should therefore be aware of the long history of ethical issues discussed in computing. The discourse on ethics and computing can be traced back to Norbert Wiener and the very beginning of digital computing. From the 1970s and 80s it has developed into an active discussion involving academics from various disciplines, professional bodies and industry.