This video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes.
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.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.
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 gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity.
This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.
This lecture picks up from the previous lesson, providing an overview of neuroimaging techniques and their clinical applications.
This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.
This lecture focuses on the rationale for employing neuroimaging methods for movement disorders.
This lecture focuses on how the immune system can target and attack the nervous system to produce autoimmune responses that may result in diseases such as multiple sclerosis, neuromyelitis, and lupus cerebritis manifested by motor, sensory, and cognitive impairments. Despite the fact that the brain is an immune-privileged site, autoreactive lymphocytes producing proinflammatory cytokines can cause active brain inflammation, leading to myelin and axonal loss.
This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity.
This lecture covers multiple aspects of FAIR neuroscience data: what makes it unique, the challenges to making it FAIR, the importance of overcoming these challenges, and how data governance comes into play.
This lecture provides guidance on the ethical considerations the clinical neuroimaging community faces when applying the FAIR principles to their research.
In response to a growing need in the neuroscience community for concrete guidance concerning ethically sound and pragmatically feasible open data-sharing, the CONP has created an ‘Ethics Toolkit’. These documents (links found below in 'Documents' section) are meant to help researchers identify key elements in the design and conduct of their projects that are often required for the open sharing of neuroscience data, such as model consent language and approaches to de-identification.
This guidance is the product of extended discussions and careful drafting by the CONP Ethics and Governance Committee that considers both Canadian and international ethical frameworks and research practice. The best way to cite these resources is with their associated Zenodo DOI:
Open Brain Consent is an international initiative aiming to address the challenge of creating participant consent language that will promote the open sharing of data, protect participant privacy, and conform to legal norms and institutional review boards.
Open Brain Consent addresses the aforementioned difficulties in neuroscience research with human participants by collecting: