In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.
This lesson delves into the opportunities and challenges of telepsychiatry. While novel digital approaches to clinical research and care have the potential to improve and accelerate patient outcomes, researchers and care providers must consider new population factors, such as digital disparity.
This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.
This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?
This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.
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 contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.
This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.
This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.
This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.
This lecture provides an introduction to the principal of anatomical organization of neural systems in the human brain and spinal cord that mediate sensation, integrate signals, and motivate behavior.
This lecture focuses on the comprehension of nociception and pain sensation, highlighting how the somatosensory system and different molecular partners are involved in nociception.
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.
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.